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+ "text": [
+ "Collecting jarvis-tools\n",
+ " Downloading jarvis_tools-2024.10.10-py2.py3-none-any.whl.metadata (3.1 kB)\n",
+ "Requirement already satisfied: numpy>=1.20.1 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (1.26.4)\n",
+ "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (1.13.1)\n",
+ "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (3.8.0)\n",
+ "Requirement already satisfied: joblib>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (1.4.2)\n",
+ "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (2.32.3)\n",
+ "Requirement already satisfied: toolz>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (0.12.1)\n",
+ "Collecting xmltodict>=0.11.0 (from jarvis-tools)\n",
+ " Downloading xmltodict-0.14.2-py2.py3-none-any.whl.metadata (8.0 kB)\n",
+ "Requirement already satisfied: tqdm>=4.41.1 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (4.66.6)\n",
+ "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (1.5.2)\n",
+ "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (1.3.0)\n",
+ "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (0.12.1)\n",
+ "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (4.54.1)\n",
+ "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (1.4.7)\n",
+ "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (24.1)\n",
+ "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (10.4.0)\n",
+ "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (3.2.0)\n",
+ "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (2.8.2)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools) (3.4.0)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools) (3.10)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools) (2.2.3)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools) (2024.8.30)\n",
+ "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->jarvis-tools) (3.5.0)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.0.0->jarvis-tools) (1.16.0)\n",
+ "Downloading jarvis_tools-2024.10.10-py2.py3-none-any.whl (4.2 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m22.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading xmltodict-0.14.2-py2.py3-none-any.whl (10.0 kB)\n",
+ "Installing collected packages: xmltodict, jarvis-tools\n",
+ "Successfully installed jarvis-tools-2024.10.10 xmltodict-0.14.2\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install jarvis-tools"
+ ]
+ },
+ {
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+ "source": [
+ "!pip3 install pymatgen"
+ ],
+ "metadata": {
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+ },
+ "id": "mC4gkbnBeP7t",
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+ },
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: pymatgen in /usr/local/lib/python3.10/dist-packages (2024.10.29)\n",
+ "Requirement already satisfied: joblib>=1 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (1.4.2)\n",
+ "Requirement already satisfied: matplotlib>=3.8 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (3.8.0)\n",
+ "Requirement already satisfied: monty>=2024.7.29 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (2024.10.21)\n",
+ "Requirement already satisfied: networkx>=3 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (3.4.2)\n",
+ "Requirement already satisfied: palettable>=3.3.3 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (3.3.3)\n",
+ "Requirement already satisfied: pandas>=2 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (2.2.2)\n",
+ "Requirement already satisfied: plotly>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (5.24.1)\n",
+ "Requirement already satisfied: pybtex>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (0.24.0)\n",
+ "Requirement already satisfied: requests>=2.32 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (2.32.3)\n",
+ "Requirement already satisfied: ruamel.yaml>=0.17.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (0.18.6)\n",
+ "Requirement already satisfied: scipy>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (1.13.1)\n",
+ "Requirement already satisfied: spglib>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (2.5.0)\n",
+ "Requirement already satisfied: sympy>=1.2 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (1.13.1)\n",
+ "Requirement already satisfied: tabulate>=0.9 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (0.9.0)\n",
+ "Requirement already satisfied: tqdm>=4.60 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (4.66.6)\n",
+ "Requirement already satisfied: uncertainties>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (3.2.2)\n",
+ "Requirement already satisfied: numpy<3,>=1.25.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (1.26.4)\n",
+ "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (1.3.0)\n",
+ "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (0.12.1)\n",
+ "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (4.54.1)\n",
+ "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (1.4.7)\n",
+ "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (24.1)\n",
+ "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (10.4.0)\n",
+ "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (3.2.0)\n",
+ "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2->pymatgen) (2024.2)\n",
+ "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=2->pymatgen) (2024.2)\n",
+ "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly>=4.5.0->pymatgen) (9.0.0)\n",
+ "Requirement already satisfied: PyYAML>=3.01 in /usr/local/lib/python3.10/dist-packages (from pybtex>=0.24.0->pymatgen) (6.0.2)\n",
+ "Requirement already satisfied: latexcodec>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from pybtex>=0.24.0->pymatgen) (3.0.0)\n",
+ "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from pybtex>=0.24.0->pymatgen) (1.16.0)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32->pymatgen) (3.4.0)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32->pymatgen) (3.10)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32->pymatgen) (2.2.3)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32->pymatgen) (2024.8.30)\n",
+ "Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from ruamel.yaml>=0.17.0->pymatgen) (0.2.12)\n",
+ "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.2->pymatgen) (1.3.0)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install dgl -f https://data.dgl.ai/wheels/torch-2.1/repo.html"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "fhA7AxnzeQfj",
+ "outputId": "3e9e530c-b84f-4285-8c00-659d64cb79b2"
+ },
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Looking in links: https://data.dgl.ai/wheels/torch-2.1/repo.html\n",
+ "Requirement already satisfied: dgl in /usr/local/lib/python3.10/dist-packages (2.4.0)\n",
+ "Requirement already satisfied: networkx>=2.1 in /usr/local/lib/python3.10/dist-packages (from dgl) (3.4.2)\n",
+ "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (1.26.4)\n",
+ "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from dgl) (24.1)\n",
+ "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from dgl) (2.2.2)\n",
+ "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (5.9.5)\n",
+ "Requirement already satisfied: pydantic>=2.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (2.9.2)\n",
+ "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from dgl) (6.0.2)\n",
+ "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (2.32.3)\n",
+ "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (1.13.1)\n",
+ "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from dgl) (4.66.6)\n",
+ "Requirement already satisfied: torch<=2.4.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (2.4.0)\n",
+ "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->dgl) (0.7.0)\n",
+ "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->dgl) (2.23.4)\n",
+ "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->dgl) (4.12.2)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (3.4.0)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (3.10)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (2.2.3)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (2024.8.30)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (3.16.1)\n",
+ "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (1.13.1)\n",
+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (3.1.4)\n",
+ "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (2024.10.0)\n",
+ "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (9.1.0.70)\n",
+ "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (12.1.3.1)\n",
+ "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (11.0.2.54)\n",
+ "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (10.3.2.106)\n",
+ "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (11.4.5.107)\n",
+ "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (12.1.0.106)\n",
+ "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (2.20.5)\n",
+ "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (12.1.105)\n",
+ "Requirement already satisfied: triton==3.0.0 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (3.0.0)\n",
+ "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch<=2.4.0->dgl) (12.6.77)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl) (2024.2)\n",
+ "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl) (2024.2)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->dgl) (1.16.0)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch<=2.4.0->dgl) (3.0.2)\n",
+ "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch<=2.4.0->dgl) (1.3.0)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip3 install matgl"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "YM0zUT9-fqfc",
+ "outputId": "6bca17f0-06c7-4559-9b81-a79c3739a116"
+ },
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting matgl\n",
+ " Downloading matgl-1.1.3-py3-none-any.whl.metadata (16 kB)\n",
+ "Collecting ase (from matgl)\n",
+ " Downloading ase-3.23.0-py3-none-any.whl.metadata (3.8 kB)\n",
+ "Requirement already satisfied: dgl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from matgl) (2.4.0)\n",
+ "Requirement already satisfied: pymatgen in /usr/local/lib/python3.10/dist-packages (from matgl) (2024.10.29)\n",
+ "Collecting lightning (from matgl)\n",
+ " Downloading lightning-2.4.0-py3-none-any.whl.metadata (38 kB)\n",
+ "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from matgl) (2.4.0)\n",
+ "Requirement already satisfied: pydantic in /usr/local/lib/python3.10/dist-packages (from matgl) (2.9.2)\n",
+ "Collecting torchdata<0.8.0 (from matgl)\n",
+ " Downloading torchdata-0.7.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
+ "Requirement already satisfied: networkx>=2.1 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (3.4.2)\n",
+ "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (1.26.4)\n",
+ "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (24.1)\n",
+ "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (2.2.2)\n",
+ "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (5.9.5)\n",
+ "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (6.0.2)\n",
+ "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (2.32.3)\n",
+ "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (1.13.1)\n",
+ "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (4.66.6)\n",
+ "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic->matgl) (0.7.0)\n",
+ "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic->matgl) (2.23.4)\n",
+ "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic->matgl) (4.12.2)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (3.16.1)\n",
+ "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (1.13.1)\n",
+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (3.1.4)\n",
+ "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (2024.10.0)\n",
+ "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (9.1.0.70)\n",
+ "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.3.1)\n",
+ "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (11.0.2.54)\n",
+ "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (10.3.2.106)\n",
+ "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (11.4.5.107)\n",
+ "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.0.106)\n",
+ "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (2.20.5)\n",
+ "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.105)\n",
+ "Requirement already satisfied: triton==3.0.0 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (3.0.0)\n",
+ "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->matgl) (12.6.77)\n",
+ "Requirement already satisfied: urllib3>=1.25 in /usr/local/lib/python3.10/dist-packages (from torchdata<0.8.0->matgl) (2.2.3)\n",
+ "Requirement already satisfied: matplotlib>=3.3.4 in /usr/local/lib/python3.10/dist-packages (from ase->matgl) (3.8.0)\n",
+ "Collecting lightning-utilities<2.0,>=0.10.0 (from lightning->matgl)\n",
+ " Downloading lightning_utilities-0.11.8-py3-none-any.whl.metadata (5.2 kB)\n",
+ "Collecting torchmetrics<3.0,>=0.7.0 (from lightning->matgl)\n",
+ " Downloading torchmetrics-1.5.1-py3-none-any.whl.metadata (20 kB)\n",
+ "Collecting pytorch-lightning (from lightning->matgl)\n",
+ " Downloading pytorch_lightning-2.4.0-py3-none-any.whl.metadata (21 kB)\n",
+ "Requirement already satisfied: joblib>=1 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (1.4.2)\n",
+ "Requirement already satisfied: monty>=2024.7.29 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (2024.10.21)\n",
+ "Requirement already satisfied: palettable>=3.3.3 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (3.3.3)\n",
+ "Requirement already satisfied: plotly>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (5.24.1)\n",
+ "Requirement already satisfied: pybtex>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (0.24.0)\n",
+ "Requirement already satisfied: ruamel.yaml>=0.17.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (0.18.6)\n",
+ "Requirement already satisfied: spglib>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (2.5.0)\n",
+ "Requirement already satisfied: tabulate>=0.9 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (0.9.0)\n",
+ "Requirement already satisfied: uncertainties>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (3.2.2)\n",
+ "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (3.10.10)\n",
+ "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from lightning-utilities<2.0,>=0.10.0->lightning->matgl) (75.1.0)\n",
+ "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (1.3.0)\n",
+ "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (0.12.1)\n",
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+ "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (10.4.0)\n",
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+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl>=2.0.0->matgl) (2024.2)\n",
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+ "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly>=4.5.0->pymatgen->matgl) (9.0.0)\n",
+ "Requirement already satisfied: latexcodec>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from pybtex>=0.24.0->pymatgen->matgl) (3.0.0)\n",
+ "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from pybtex>=0.24.0->pymatgen->matgl) (1.16.0)\n",
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+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl>=2.0.0->matgl) (3.10)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl>=2.0.0->matgl) (2024.8.30)\n",
+ "Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from ruamel.yaml>=0.17.0->pymatgen->matgl) (0.2.12)\n",
+ "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->matgl) (1.3.0)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->matgl) (3.0.2)\n",
+ "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (2.4.3)\n",
+ "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (1.3.1)\n",
+ "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (24.2.0)\n",
+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (1.5.0)\n",
+ "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (6.1.0)\n",
+ "Requirement already satisfied: yarl<2.0,>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (1.17.0)\n",
+ "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (4.0.3)\n",
+ "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from yarl<2.0,>=1.12.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (0.2.0)\n",
+ "Downloading matgl-1.1.3-py3-none-any.whl (223 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m223.3/223.3 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading torchdata-0.7.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.7 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m4.7/4.7 MB\u001b[0m \u001b[31m61.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading ase-3.23.0-py3-none-any.whl (2.9 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m72.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading lightning-2.4.0-py3-none-any.whl (810 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m811.0/811.0 kB\u001b[0m \u001b[31m34.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading lightning_utilities-0.11.8-py3-none-any.whl (26 kB)\n",
+ "Downloading torchmetrics-1.5.1-py3-none-any.whl (890 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m890.6/890.6 kB\u001b[0m \u001b[31m45.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading pytorch_lightning-2.4.0-py3-none-any.whl (815 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m815.2/815.2 kB\u001b[0m \u001b[31m42.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hInstalling collected packages: lightning-utilities, ase, torchmetrics, torchdata, pytorch-lightning, lightning, matgl\n",
+ "Successfully installed ase-3.23.0 lightning-2.4.0 lightning-utilities-0.11.8 matgl-1.1.3 pytorch-lightning-2.4.0 torchdata-0.7.1 torchmetrics-1.5.1\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from __future__ import annotations\n",
+ "\n",
+ "import os\n",
+ "import shutil\n",
+ "import warnings\n",
+ "import zipfile\n",
+ "import matgl\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import pytorch_lightning as pl\n",
+ "import torch\n",
+ "import pickle\n",
+ "import numpy as np\n",
+ "from dgl.data.utils import split_dataset\n",
+ "from pymatgen.core import Structure\n",
+ "from pytorch_lightning.loggers import CSVLogger\n",
+ "from lightning.pytorch import Trainer\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "from matgl.ext.pymatgen import Structure2Graph, get_element_list\n",
+ "from matgl.graph.data import MGLDataset, MGLDataLoader #collate_fn. - shivani i don't think you need this as num_workers=0\n",
+ "from matgl.layers import BondExpansion\n",
+ "from matgl.models import MEGNet\n",
+ "from matgl.utils.io import RemoteFile\n",
+ "from matgl.utils.training import ModelLightningModule\n",
+ "\n",
+ "# To suppress warnings for clearer output\n",
+ "warnings.simplefilter(\"ignore\")"
+ ],
+ "metadata": {
+ "id": "avglyJbheVCr"
+ },
+ "execution_count": 39,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from jarvis.db.figshare import data\n",
+ "\n",
+ "dft_3d = data('dft_3d')"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "vZFdOECqfefs",
+ "outputId": "e722910c-d5f9-48c0-85af-fb7536d81ce7"
+ },
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Obtaining 3D dataset 76k ...\n",
+ "Reference:https://www.nature.com/articles/s41524-020-00440-1\n",
+ "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 40.8M/40.8M [00:01<00:00, 20.5MiB/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Loading the zipfile...\n",
+ "Loading completed.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dft_3d[0].keys()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "o1-OpcxGgXn3",
+ "outputId": "eb48c546-8539-4acc-f8cd-290039cd1a6f"
+ },
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "dict_keys(['jid', 'spg_number', 'spg_symbol', 'formula', 'formation_energy_peratom', 'func', 'optb88vdw_bandgap', 'atoms', 'slme', 'magmom_oszicar', 'spillage', 'elastic_tensor', 'effective_masses_300K', 'kpoint_length_unit', 'maxdiff_mesh', 'maxdiff_bz', 'encut', 'optb88vdw_total_energy', 'epsx', 'epsy', 'epsz', 'mepsx', 'mepsy', 'mepsz', 'modes', 'magmom_outcar', 'max_efg', 'avg_elec_mass', 'avg_hole_mass', 'icsd', 'dfpt_piezo_max_eij', 'dfpt_piezo_max_dij', 'dfpt_piezo_max_dielectric', 'dfpt_piezo_max_dielectric_electronic', 'dfpt_piezo_max_dielectric_ionic', 'max_ir_mode', 'min_ir_mode', 'n-Seebeck', 'p-Seebeck', 'n-powerfact', 'p-powerfact', 'ncond', 'pcond', 'nkappa', 'pkappa', 'ehull', 'Tc_supercon', 'dimensionality', 'efg', 'xml_data_link', 'typ', 'exfoliation_energy', 'spg', 'crys', 'density', 'poisson', 'raw_files', 'nat', 'bulk_modulus_kv', 'shear_modulus_gv', 'mbj_bandgap', 'hse_gap', 'reference', 'search'])"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 13
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "## Let's make a dataframe from this:\n",
+ "import pandas as pd\n",
+ "import numpy as np"
+ ],
+ "metadata": {
+ "id": "RnAkEQBSgZki"
+ },
+ "execution_count": 14,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df=pd.DataFrame(dft_3d)\n",
+ "df.head()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 429
+ },
+ "id": "HUvXBAGDgdYn",
+ "outputId": "cb02920a-b7e5-469e-8b8e-8fb55a7177e3"
+ },
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " jid spg_number spg_symbol formula formation_energy_peratom \\\n",
+ "0 JVASP-90856 129 P4/nmm TiCuSiAs -0.42762 \n",
+ "1 JVASP-86097 221 Pm-3m DyB6 -0.41596 \n",
+ "2 JVASP-64906 119 I-4m2 Be2OsRu 0.04847 \n",
+ "3 JVASP-98225 14 P2_1/c KBi -0.44140 \n",
+ "4 JVASP-10 164 P-3m1 VSe2 -0.71026 \n",
+ "\n",
+ " func optb88vdw_bandgap \\\n",
+ "0 OptB88vdW 0.000 \n",
+ "1 OptB88vdW 0.000 \n",
+ "2 OptB88vdW 0.000 \n",
+ "3 OptB88vdW 0.472 \n",
+ "4 OptB88vdW 0.000 \n",
+ "\n",
+ " atoms slme magmom_oszicar ... \\\n",
+ "0 {'lattice_mat': [[3.566933224304235, 0.0, -0.0... na 0.0 ... \n",
+ "1 {'lattice_mat': [[4.089078911208881, 0.0, 0.0]... na 0.0 ... \n",
+ "2 {'lattice_mat': [[-1.833590720595598, 1.833590... na 0.0 ... \n",
+ "3 {'lattice_mat': [[7.2963518353359165, 0.0, 0.0... na 0.0 ... \n",
+ "4 {'lattice_mat': [[1.6777483798834445, -2.90594... na 0.0 ... \n",
+ "\n",
+ " density poisson raw_files nat \\\n",
+ "0 5.956 na [] 8 \n",
+ "1 5.522 na [OPT-LOPTICS,JVASP-86097.zip,https://ndownload... 7 \n",
+ "2 10.960 na [OPT-LOPTICS,JVASP-64906.zip,https://ndownload... 4 \n",
+ "3 5.145 na [] 32 \n",
+ "4 5.718 0.23 [FD-ELAST,JVASP-10.zip,https://ndownloader.fig... 3 \n",
+ "\n",
+ " bulk_modulus_kv shear_modulus_gv mbj_bandgap hse_gap \\\n",
+ "0 na na na na \n",
+ "1 na na na na \n",
+ "2 na na na na \n",
+ "3 na na na na \n",
+ "4 48.79 33.05 0.0 na \n",
+ "\n",
+ " reference search \n",
+ "0 mp-1080455 -As-Cu-Si-Ti \n",
+ "1 mp-568319 -B-Dy \n",
+ "2 auid-3eaf68dd483bf4f4 -Be-Os-Ru \n",
+ "3 mp-31104 -Bi-K \n",
+ "4 mp-694 -Se-V \n",
+ "\n",
+ "[5 rows x 64 columns]"
+ ],
+ "text/html": [
+ "\n",
+ " <div id=\"df-7bedbd8a-b0f7-4b76-aac4-3afa628ac70d\" class=\"colab-df-container\">\n",
+ " <div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>jid</th>\n",
+ " <th>spg_number</th>\n",
+ " <th>spg_symbol</th>\n",
+ " <th>formula</th>\n",
+ " <th>formation_energy_peratom</th>\n",
+ " <th>func</th>\n",
+ " <th>optb88vdw_bandgap</th>\n",
+ " <th>atoms</th>\n",
+ " <th>slme</th>\n",
+ " <th>magmom_oszicar</th>\n",
+ " <th>...</th>\n",
+ " <th>density</th>\n",
+ " <th>poisson</th>\n",
+ " <th>raw_files</th>\n",
+ " <th>nat</th>\n",
+ " <th>bulk_modulus_kv</th>\n",
+ " <th>shear_modulus_gv</th>\n",
+ " <th>mbj_bandgap</th>\n",
+ " <th>hse_gap</th>\n",
+ " <th>reference</th>\n",
+ " <th>search</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>JVASP-90856</td>\n",
+ " <td>129</td>\n",
+ " <td>P4/nmm</td>\n",
+ " <td>TiCuSiAs</td>\n",
+ " <td>-0.42762</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[3.566933224304235, 0.0, -0.0...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.956</td>\n",
+ " <td>na</td>\n",
+ " <td>[]</td>\n",
+ " <td>8</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-1080455</td>\n",
+ " <td>-As-Cu-Si-Ti</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>JVASP-86097</td>\n",
+ " <td>221</td>\n",
+ " <td>Pm-3m</td>\n",
+ " <td>DyB6</td>\n",
+ " <td>-0.41596</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[4.089078911208881, 0.0, 0.0]...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.522</td>\n",
+ " <td>na</td>\n",
+ " <td>[OPT-LOPTICS,JVASP-86097.zip,https://ndownload...</td>\n",
+ " <td>7</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-568319</td>\n",
+ " <td>-B-Dy</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>JVASP-64906</td>\n",
+ " <td>119</td>\n",
+ " <td>I-4m2</td>\n",
+ " <td>Be2OsRu</td>\n",
+ " <td>0.04847</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[-1.833590720595598, 1.833590...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>10.960</td>\n",
+ " <td>na</td>\n",
+ " <td>[OPT-LOPTICS,JVASP-64906.zip,https://ndownload...</td>\n",
+ " <td>4</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>auid-3eaf68dd483bf4f4</td>\n",
+ " <td>-Be-Os-Ru</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>JVASP-98225</td>\n",
+ " <td>14</td>\n",
+ " <td>P2_1/c</td>\n",
+ " <td>KBi</td>\n",
+ " <td>-0.44140</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.472</td>\n",
+ " <td>{'lattice_mat': [[7.2963518353359165, 0.0, 0.0...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.145</td>\n",
+ " <td>na</td>\n",
+ " <td>[]</td>\n",
+ " <td>32</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-31104</td>\n",
+ " <td>-Bi-K</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>JVASP-10</td>\n",
+ " <td>164</td>\n",
+ " <td>P-3m1</td>\n",
+ " <td>VSe2</td>\n",
+ " <td>-0.71026</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[1.6777483798834445, -2.90594...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.718</td>\n",
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+ " <td>3</td>\n",
+ " <td>48.79</td>\n",
+ " <td>33.05</td>\n",
+ " <td>0.0</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-694</td>\n",
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+ " const element = document.querySelector('#df-7bedbd8a-b0f7-4b76-aac4-3afa628ac70d');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ "\n",
+ "\n",
+ "<div id=\"df-bd8b8a00-937e-4b1d-a50c-05a9e032c404\">\n",
+ " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bd8b8a00-937e-4b1d-a50c-05a9e032c404')\"\n",
+ " title=\"Suggest charts\"\n",
+ " style=\"display:none;\">\n",
+ "\n",
+ "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+ " width=\"24px\">\n",
+ " <g>\n",
+ " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+ " </g>\n",
+ "</svg>\n",
+ " </button>\n",
+ "\n",
+ "<style>\n",
+ " .colab-df-quickchart {\n",
+ " --bg-color: #E8F0FE;\n",
+ " --fill-color: #1967D2;\n",
+ " --hover-bg-color: #E2EBFA;\n",
+ " --hover-fill-color: #174EA6;\n",
+ " --disabled-fill-color: #AAA;\n",
+ " --disabled-bg-color: #DDD;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-quickchart {\n",
+ " --bg-color: #3B4455;\n",
+ " --fill-color: #D2E3FC;\n",
+ " --hover-bg-color: #434B5C;\n",
+ " --hover-fill-color: #FFFFFF;\n",
+ " --disabled-bg-color: #3B4455;\n",
+ " --disabled-fill-color: #666;\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart {\n",
+ " background-color: var(--bg-color);\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: var(--fill-color);\n",
+ " height: 32px;\n",
+ " padding: 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart:hover {\n",
+ " background-color: var(--hover-bg-color);\n",
+ " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: var(--button-hover-fill-color);\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart-complete:disabled,\n",
+ " .colab-df-quickchart-complete:disabled:hover {\n",
+ " background-color: var(--disabled-bg-color);\n",
+ " fill: var(--disabled-fill-color);\n",
+ " box-shadow: none;\n",
+ " }\n",
+ "\n",
+ " .colab-df-spinner {\n",
+ " border: 2px solid var(--fill-color);\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " animation:\n",
+ " spin 1s steps(1) infinite;\n",
+ " }\n",
+ "\n",
+ " @keyframes spin {\n",
+ " 0% {\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " border-left-color: var(--fill-color);\n",
+ " }\n",
+ " 20% {\n",
+ " border-color: transparent;\n",
+ " border-left-color: var(--fill-color);\n",
+ " border-top-color: var(--fill-color);\n",
+ " }\n",
+ " 30% {\n",
+ " border-color: transparent;\n",
+ " border-left-color: var(--fill-color);\n",
+ " border-top-color: var(--fill-color);\n",
+ " border-right-color: var(--fill-color);\n",
+ " }\n",
+ " 40% {\n",
+ " border-color: transparent;\n",
+ " border-right-color: var(--fill-color);\n",
+ " border-top-color: var(--fill-color);\n",
+ " }\n",
+ " 60% {\n",
+ " border-color: transparent;\n",
+ " border-right-color: var(--fill-color);\n",
+ " }\n",
+ " 80% {\n",
+ " border-color: transparent;\n",
+ " border-right-color: var(--fill-color);\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " }\n",
+ " 90% {\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " }\n",
+ " }\n",
+ "</style>\n",
+ "\n",
+ " <script>\n",
+ " async function quickchart(key) {\n",
+ " const quickchartButtonEl =\n",
+ " document.querySelector('#' + key + ' button');\n",
+ " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
+ " quickchartButtonEl.classList.add('colab-df-spinner');\n",
+ " try {\n",
+ " const charts = await google.colab.kernel.invokeFunction(\n",
+ " 'suggestCharts', [key], {});\n",
+ " } catch (error) {\n",
+ " console.error('Error during call to suggestCharts:', error);\n",
+ " }\n",
+ " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+ " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+ " }\n",
+ " (() => {\n",
+ " let quickchartButtonEl =\n",
+ " document.querySelector('#df-bd8b8a00-937e-4b1d-a50c-05a9e032c404 button');\n",
+ " quickchartButtonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ " })();\n",
+ " </script>\n",
+ "</div>\n",
+ "\n",
+ " </div>\n",
+ " </div>\n"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "dataframe",
+ "variable_name": "df"
+ }
+ },
+ "metadata": {},
+ "execution_count": 15
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "## Count number of entries for each property\n",
+ "for i in df.columns.values:\n",
+ " val=df[i].replace('na',np.nan).dropna().values\n",
+ " print(i,len(val))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "hrRk8GKighlh",
+ "outputId": "d54272bd-a432-462c-d89a-65241d14db65"
+ },
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "jid 75993\n",
+ "spg_number 75993\n",
+ "spg_symbol 75993\n",
+ "formula 75993\n",
+ "formation_energy_peratom 75993\n",
+ "func 75993\n",
+ "optb88vdw_bandgap 75993\n",
+ "atoms 75993\n",
+ "slme 9770\n",
+ "magmom_oszicar 71320\n",
+ "spillage 11377\n",
+ "elastic_tensor 25513\n",
+ "effective_masses_300K 75993\n",
+ "kpoint_length_unit 75671\n",
+ "maxdiff_mesh 5861\n",
+ "maxdiff_bz 5861\n",
+ "encut 75670\n",
+ "optb88vdw_total_energy 75993\n",
+ "epsx 52168\n",
+ "epsy 52168\n",
+ "epsz 52168\n",
+ "mepsx 18293\n",
+ "mepsy 18293\n",
+ "mepsz 18293\n",
+ "modes 13910\n",
+ "magmom_outcar 74261\n",
+ "max_efg 11871\n",
+ "avg_elec_mass 17645\n",
+ "avg_hole_mass 17645\n",
+ "icsd 75993\n",
+ "dfpt_piezo_max_eij 4799\n",
+ "dfpt_piezo_max_dij 3347\n",
+ "dfpt_piezo_max_dielectric 4706\n",
+ "dfpt_piezo_max_dielectric_electronic 4809\n",
+ "dfpt_piezo_max_dielectric_ionic 4809\n",
+ "max_ir_mode 4805\n",
+ "min_ir_mode 4809\n",
+ "n-Seebeck 23218\n",
+ "p-Seebeck 23218\n",
+ "n-powerfact 23218\n",
+ "p-powerfact 23218\n",
+ "ncond 23218\n",
+ "pcond 23218\n",
+ "nkappa 23218\n",
+ "pkappa 23218\n",
+ "ehull 75993\n",
+ "Tc_supercon 1058\n",
+ "dimensionality 75560\n",
+ "efg 75993\n",
+ "xml_data_link 75993\n",
+ "typ 75993\n",
+ "exfoliation_energy 813\n",
+ "spg 75993\n",
+ "crys 75993\n",
+ "density 75993\n",
+ "poisson 23597\n",
+ "raw_files 75993\n",
+ "nat 75993\n",
+ "bulk_modulus_kv 23824\n",
+ "shear_modulus_gv 23824\n",
+ "mbj_bandgap 19805\n",
+ "hse_gap 56\n",
+ "reference 75993\n",
+ "search 75993\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "## Filter dataset based on desired property\n",
+ "## We will focus on elastic properties for today, i.e. Bulk modulus"
+ ],
+ "metadata": {
+ "id": "6dxg4ITfgkOE"
+ },
+ "execution_count": 17,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from jarvis.core.atoms import Atoms\n",
+ "bm=df[df.bulk_modulus_kv != 'na']\n",
+ "data = [(Atoms.from_dict(bm.iloc[i]['atoms']).pymatgen_converter(), bm.iloc[i].bulk_modulus_kv) for i in range(len(bm))]"
+ ],
+ "metadata": {
+ "id": "xcuLFYdNgq-u"
+ },
+ "execution_count": 18,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import itertools\n",
+ "def get_stoichiometry(elements):\n",
+ " return [(g[0], len(list(g[1]))) for g in itertools.groupby(elements)]"
+ ],
+ "metadata": {
+ "id": "Sc1zXAn4gtTT"
+ },
+ "execution_count": 19,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "## Use all the material dataset for training the bulk modulus\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "stoichs=[] #stoichiometry\n",
+ "bulk=[] #bulk modulus\n",
+ "for i in tqdm(range(len(bm))):\n",
+ " stoichs.append(Atoms.from_dict(bm.iloc[i]['atoms']).pymatgen_converter())\n",
+ " bulk.append(bm.iloc[i]['bulk_modulus_kv'])\n",
+ "data_ran=list(zip(stoichs,bulk))\n",
+ "#write out the dataset, to train later\n",
+ "import pickle\n",
+ "with open('data_ran.pickle', 'wb') as f:\n",
+ " pickle.dump(data_ran, f)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "arU4jF5tgvt0",
+ "outputId": "c75f7f94-afe6-4d42-e81a-93405ddcc301"
+ },
+ "execution_count": 20,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 23824/23824 [00:25<00:00, 921.20it/s]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#read in the dataset\n",
+ "data_ran=pd.read_pickle('./data_ran.pickle')"
+ ],
+ "metadata": {
+ "id": "CFgTo75EgzKR"
+ },
+ "execution_count": 22,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "type(data_ran)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "BWZuJzqNg-ak",
+ "outputId": "8076df63-2b15-4739-f5fe-9f703b68db6f"
+ },
+ "execution_count": 23,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "list"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 23
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import random\n",
+ "import numpy as np\n",
+ "\n",
+ "\n",
+ "random.shuffle(data_ran)\n",
+ "\n",
+ "structures=[d[0] for d in data_ran[:15000]]\n",
+ "targets=np.log10([d[1] for d in data_ran])\n",
+ "\n",
+ "print(structures[0],targets[0])"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "TG4g1Dp4hBhg",
+ "outputId": "a3adbd5b-95c9-4ec9-8cd0-4c39136ea699"
+ },
+ "execution_count": 24,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Full Formula (Li4 Ce4 O8)\n",
+ "Reduced Formula: LiCeO2\n",
+ "abc : 5.778710 5.859847 6.029586\n",
+ "angles: 90.000000 90.000000 103.986129\n",
+ "pbc : True True True\n",
+ "Sites (16)\n",
+ " # SP a b c\n",
+ "--- ---- -------- -------- --------\n",
+ " 0 Li 0.182502 0.662954 0.132604\n",
+ " 1 Li 0.317498 0.337046 0.632604\n",
+ " 2 Li 0.817498 0.337046 0.867396\n",
+ " 3 Li 0.682502 0.662954 0.367396\n",
+ " 4 Ce 0.303409 0.200495 0.071539\n",
+ " 5 Ce 0.803409 0.200495 0.428461\n",
+ " 6 Ce 0.696591 0.799505 0.928461\n",
+ " 7 Ce 0.196591 0.799505 0.571539\n",
+ " 8 O 0.986063 0.906145 0.246099\n",
+ " 9 O 0.696592 0.43465 0.136137\n",
+ " 10 O 0.513937 0.093855 0.746099\n",
+ " 11 O 0.013937 0.093855 0.753901\n",
+ " 12 O 0.303408 0.56535 0.863863\n",
+ " 13 O 0.803408 0.56535 0.636137\n",
+ " 14 O 0.196592 0.43465 0.363863\n",
+ " 15 O 0.486063 0.906145 0.253901 2.0546896429499797\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# get element types in the dataset\n",
+ "elem_list = get_element_list(structures)\n",
+ "# setup a graph converter\n",
+ "converter = Structure2Graph(element_types=elem_list, cutoff=4.0)\n",
+ "# convert the raw dataset into MEGNetDataset\n",
+ "mp_dataset = MGLDataset(\n",
+ " structures=structures,\n",
+ " labels={\"bulk_modulus_kv\": targets},\n",
+ " converter=converter,\n",
+ ")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Z-jazl2PhHsP",
+ "outputId": "0044297b-6dc9-4f70-83f0-afb9c4c558d7"
+ },
+ "execution_count": 25,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 15000/15000 [00:24<00:00, 616.21it/s]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train_data, val_data, test_data = split_dataset(\n",
+ " mp_dataset,\n",
+ " frac_list=[0.6, 0.2, 0.2],\n",
+ " shuffle=True,\n",
+ " random_state=42,\n",
+ ")\n",
+ "train_loader, val_loader, test_loader = MGLDataLoader(\n",
+ " train_data=train_data,\n",
+ " val_data=val_data,\n",
+ " test_data=test_data,\n",
+ " batch_size=64,\n",
+ " num_workers=0,\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "QPyjAxGghK0Q"
+ },
+ "execution_count": 26,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# setup the embedding layer for node attributes\n",
+ "node_embed = torch.nn.Embedding(len(elem_list), 16)\n",
+ "# define the bond expansion\n",
+ "bond_expansion = BondExpansion(rbf_type=\"Gaussian\", initial=0.0, final=5.0, num_centers=100, width=0.5)\n",
+ "\n",
+ "# setup the architecture of MEGNet model\n",
+ "model = MEGNet(\n",
+ " dim_node_embedding=16,\n",
+ " dim_edge_embedding=100,\n",
+ " dim_state_embedding=2,\n",
+ " nblocks=3,\n",
+ " hidden_layer_sizes_input=(64, 32),\n",
+ " hidden_layer_sizes_conv=(64, 64, 32),\n",
+ " nlayers_set2set=1,\n",
+ " niters_set2set=2,\n",
+ " hidden_layer_sizes_output=(32, 16),\n",
+ " is_classification=False,\n",
+ " activation_type=\"softplus2\",\n",
+ " bond_expansion=bond_expansion,\n",
+ " #collate_fn=collate_fn, shivani - not needed now?\n",
+ " cutoff=4.0,\n",
+ " gauss_width=0.5,\n",
+ ")\n",
+ "\n",
+ "# setup the MEGNetTrainer\n",
+ "lit_module = ModelLightningModule(model=model)"
+ ],
+ "metadata": {
+ "id": "pYGXwyZphQtd"
+ },
+ "execution_count": 33,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "logger = CSVLogger(\"logged\", name=\"MEGNet_training\")\n",
+ "trainer = Trainer(max_epochs=5, accelerator=\"cpu\", logger=logger) #set to SMALL NUMBER FOR TESTING, PLEASE CHANGE.\n",
+ "trainer.fit(model=lit_module, train_dataloaders=train_loader, val_dataloaders=val_loader)\n",
+ "\n",
+ "warnings.simplefilter(\"ignore\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 708,
+ "referenced_widgets": [
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+ "9fb1d2f72fda43e4a91e4cbb23426322",
+ "0cc181a9b3e04d658d0eefefaabecaf4",
+ "b320af0dc127481fb92415d2247a565a",
+ "2a6e76a13e5747c2888b73ff55361dea",
+ "2d3bfc8b4da94766a48e6bd84e3932b3",
+ "530114990c934b02b04ed88233a4cda3",
+ "2fbda58508c94503891ad1ab96445398",
+ "348fc0c1069a4430be11d2112f212080",
+ "0c134e301fe5481bbcd47eb35ff1ecd9",
+ "cda06210315d42f3b4909bdc14310e15",
+ "98131182118a4e0bbb0de266875c10ec",
+ "9ef52af34b6d4030b0ed2ad1006e2a05",
+ "c719f246dc254ba284b1975932eaedf9",
+ "b70eedcca7744a8dab61e3c5796e2072",
+ "3ccc25decd5b48e39bd101a6a526865e",
+ "904fb13e4c9a4290a95c7003770d0a32"
+ ]
+ },
+ "id": "nqWWnzQUhoki",
+ "outputId": "71988016-cb28-41b3-ea83-4400f135f481"
+ },
+ "execution_count": 40,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "INFO: GPU available: False, used: False\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n",
+ "INFO: TPU available: False, using: 0 TPU cores\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
+ "INFO: HPU available: False, using: 0 HPUs\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
+ "INFO: \n",
+ " | Name | Type | Params | Mode \n",
+ "----------------------------------------------------\n",
+ "0 | model | MEGNet | 189 K | train\n",
+ "1 | mae | MeanAbsoluteError | 0 | train\n",
+ "2 | rmse | MeanSquaredError | 0 | train\n",
+ "----------------------------------------------------\n",
+ "189 K Trainable params\n",
+ "100 Non-trainable params\n",
+ "189 K Total params\n",
+ "0.758 Total estimated model params size (MB)\n",
+ "109 Modules in train mode\n",
+ "0 Modules in eval mode\n",
+ "INFO:lightning.pytorch.callbacks.model_summary:\n",
+ " | Name | Type | Params | Mode \n",
+ "----------------------------------------------------\n",
+ "0 | model | MEGNet | 189 K | train\n",
+ "1 | mae | MeanAbsoluteError | 0 | train\n",
+ "2 | rmse | MeanSquaredError | 0 | train\n",
+ "----------------------------------------------------\n",
+ "189 K Trainable params\n",
+ "100 Non-trainable params\n",
+ "189 K Total params\n",
+ "0.758 Total estimated model params size (MB)\n",
+ "109 Modules in train mode\n",
+ "0 Modules in eval mode\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Sanity Checking: | | 0/? [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "768ce9da55994740bd19d449bd0880db"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Training: | | 0/? [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d9d6aacd59ea4fcf9c0f4224b377c610"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "52064c4ca7734cd9baea5a5d8e81a81a"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "2e3d634584694485a3dc805dd4e6bb71"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "6fbc5cb56b044b36b6ac6fa704a42509"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "f0a25dc24c19453ba9f3e84169914ed5"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "530114990c934b02b04ed88233a4cda3"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "INFO: `Trainer.fit` stopped: `max_epochs=5` reached.\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=5` reached.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "metrics = pd.read_csv(\"logged/MEGNet_training/version_0/metrics.csv\")\n",
+ "metrics[\"train_MAE\"].dropna().plot()\n",
+ "metrics[\"val_MAE\"].dropna().plot()\n",
+ "\n",
+ "_ = plt.legend()\n",
+ "#plt.savefig(\"loss.jpg\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 430
+ },
+ "id": "x2mTOHAGhqvE",
+ "outputId": "252e83de-6b95-4b75-ac17-cbc96a04a0cf"
+ },
+ "execution_count": 41,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "metrics"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 383
+ },
+ "id": "qZ0XV5e2jYXR",
+ "outputId": "ce1b9f10-d929-4db9-ace9-4c2801731f3b"
+ },
+ "execution_count": 42,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " epoch step train_MAE train_RMSE train_Total_Loss val_MAE val_RMSE \\\n",
+ "0 0 140 NaN NaN NaN NaN NaN \n",
+ "1 0 140 NaN NaN NaN NaN NaN \n",
+ "2 1 281 NaN NaN NaN NaN NaN \n",
+ "3 1 281 NaN NaN NaN NaN NaN \n",
+ "4 2 422 NaN NaN NaN NaN NaN \n",
+ "5 2 422 NaN NaN NaN NaN NaN \n",
+ "6 3 563 NaN NaN NaN NaN NaN \n",
+ "7 3 563 NaN NaN NaN NaN NaN \n",
+ "8 4 704 NaN NaN NaN NaN NaN \n",
+ "9 4 704 NaN NaN NaN NaN NaN \n",
+ "\n",
+ " val_Total_Loss \n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 NaN \n",
+ "3 NaN \n",
+ "4 NaN \n",
+ "5 NaN \n",
+ "6 NaN \n",
+ "7 NaN \n",
+ "8 NaN \n",
+ "9 NaN "
+ ],
+ "text/html": [
+ "\n",
+ " <div id=\"df-3f3f3a66-fe27-47d9-8a3b-a4d6fae45b02\" class=\"colab-df-container\">\n",
+ " <div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>epoch</th>\n",
+ " <th>step</th>\n",
+ " <th>train_MAE</th>\n",
+ " <th>train_RMSE</th>\n",
+ " <th>train_Total_Loss</th>\n",
+ " <th>val_MAE</th>\n",
+ " <th>val_RMSE</th>\n",
+ " <th>val_Total_Loss</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>140</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0</td>\n",
+ " <td>140</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>1</td>\n",
+ " <td>281</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1</td>\n",
+ " <td>281</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2</td>\n",
+ " <td>422</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>2</td>\n",
+ " <td>422</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>3</td>\n",
+ " <td>563</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>3</td>\n",
+ " <td>563</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>4</td>\n",
+ " <td>704</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>4</td>\n",
+ " <td>704</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>\n",
+ " <div class=\"colab-df-buttons\">\n",
+ "\n",
+ " <div class=\"colab-df-container\">\n",
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f3f3a66-fe27-47d9-8a3b-a4d6fae45b02')\"\n",
+ " title=\"Convert this dataframe to an interactive table.\"\n",
+ " style=\"display:none;\">\n",
+ "\n",
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+ " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+ " </svg>\n",
+ " </button>\n",
+ "\n",
+ " <style>\n",
+ " .colab-df-container {\n",
+ " display:flex;\n",
+ " gap: 12px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert {\n",
+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: #1967D2;\n",
+ " height: 32px;\n",
+ " padding: 0 0 0 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert:hover {\n",
+ " background-color: #E2EBFA;\n",
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: #174EA6;\n",
+ " }\n",
+ "\n",
+ " .colab-df-buttons div {\n",
+ " margin-bottom: 4px;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert {\n",
+ " background-color: #3B4455;\n",
+ " fill: #D2E3FC;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert:hover {\n",
+ " background-color: #434B5C;\n",
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ "\n",
+ " <script>\n",
+ " const buttonEl =\n",
+ " document.querySelector('#df-3f3f3a66-fe27-47d9-8a3b-a4d6fae45b02 button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-3f3f3a66-fe27-47d9-8a3b-a4d6fae45b02');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ "\n",
+ "\n",
+ "<div id=\"df-87ed75c9-b3bc-43d0-a0ee-4bc56bb39170\">\n",
+ " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-87ed75c9-b3bc-43d0-a0ee-4bc56bb39170')\"\n",
+ " title=\"Suggest charts\"\n",
+ " style=\"display:none;\">\n",
+ "\n",
+ "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+ " width=\"24px\">\n",
+ " <g>\n",
+ " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+ " </g>\n",
+ "</svg>\n",
+ " </button>\n",
+ "\n",
+ "<style>\n",
+ " .colab-df-quickchart {\n",
+ " --bg-color: #E8F0FE;\n",
+ " --fill-color: #1967D2;\n",
+ " --hover-bg-color: #E2EBFA;\n",
+ " --hover-fill-color: #174EA6;\n",
+ " --disabled-fill-color: #AAA;\n",
+ " --disabled-bg-color: #DDD;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-quickchart {\n",
+ " --bg-color: #3B4455;\n",
+ " --fill-color: #D2E3FC;\n",
+ " --hover-bg-color: #434B5C;\n",
+ " --hover-fill-color: #FFFFFF;\n",
+ " --disabled-bg-color: #3B4455;\n",
+ " --disabled-fill-color: #666;\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart {\n",
+ " background-color: var(--bg-color);\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: var(--fill-color);\n",
+ " height: 32px;\n",
+ " padding: 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart:hover {\n",
+ " background-color: var(--hover-bg-color);\n",
+ " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: var(--button-hover-fill-color);\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart-complete:disabled,\n",
+ " .colab-df-quickchart-complete:disabled:hover {\n",
+ " background-color: var(--disabled-bg-color);\n",
+ " fill: var(--disabled-fill-color);\n",
+ " box-shadow: none;\n",
+ " }\n",
+ "\n",
+ " .colab-df-spinner {\n",
+ " border: 2px solid var(--fill-color);\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " animation:\n",
+ " spin 1s steps(1) infinite;\n",
+ " }\n",
+ "\n",
+ " @keyframes spin {\n",
+ " 0% {\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " border-left-color: var(--fill-color);\n",
+ " }\n",
+ " 20% {\n",
+ " border-color: transparent;\n",
+ " border-left-color: var(--fill-color);\n",
+ " border-top-color: var(--fill-color);\n",
+ " }\n",
+ " 30% {\n",
+ " border-color: transparent;\n",
+ " border-left-color: var(--fill-color);\n",
+ " border-top-color: var(--fill-color);\n",
+ " border-right-color: var(--fill-color);\n",
+ " }\n",
+ " 40% {\n",
+ " border-color: transparent;\n",
+ " border-right-color: var(--fill-color);\n",
+ " border-top-color: var(--fill-color);\n",
+ " }\n",
+ " 60% {\n",
+ " border-color: transparent;\n",
+ " border-right-color: var(--fill-color);\n",
+ " }\n",
+ " 80% {\n",
+ " border-color: transparent;\n",
+ " border-right-color: var(--fill-color);\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " }\n",
+ " 90% {\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " }\n",
+ " }\n",
+ "</style>\n",
+ "\n",
+ " <script>\n",
+ " async function quickchart(key) {\n",
+ " const quickchartButtonEl =\n",
+ " document.querySelector('#' + key + ' button');\n",
+ " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
+ " quickchartButtonEl.classList.add('colab-df-spinner');\n",
+ " try {\n",
+ " const charts = await google.colab.kernel.invokeFunction(\n",
+ " 'suggestCharts', [key], {});\n",
+ " } catch (error) {\n",
+ " console.error('Error during call to suggestCharts:', error);\n",
+ " }\n",
+ " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+ " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+ " }\n",
+ " (() => {\n",
+ " let quickchartButtonEl =\n",
+ " document.querySelector('#df-87ed75c9-b3bc-43d0-a0ee-4bc56bb39170 button');\n",
+ " quickchartButtonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ " })();\n",
+ " </script>\n",
+ "</div>\n",
+ "\n",
+ " <div id=\"id_b784e831-0f56-432a-aa7e-2249f5c19941\">\n",
+ " <style>\n",
+ " .colab-df-generate {\n",
+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: #1967D2;\n",
+ " height: 32px;\n",
+ " padding: 0 0 0 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-generate:hover {\n",
+ " background-color: #E2EBFA;\n",
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: #174EA6;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-generate {\n",
+ " background-color: #3B4455;\n",
+ " fill: #D2E3FC;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-generate:hover {\n",
+ " background-color: #434B5C;\n",
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ " <button class=\"colab-df-generate\" onclick=\"generateWithVariable('metrics')\"\n",
+ " title=\"Generate code using this dataframe.\"\n",
+ " style=\"display:none;\">\n",
+ "\n",
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+ " width=\"24px\">\n",
+ " <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+ " </svg>\n",
+ " </button>\n",
+ " <script>\n",
+ " (() => {\n",
+ " const buttonEl =\n",
+ " document.querySelector('#id_b784e831-0f56-432a-aa7e-2249f5c19941 button.colab-df-generate');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " buttonEl.onclick = () => {\n",
+ " google.colab.notebook.generateWithVariable('metrics');\n",
+ " }\n",
+ " })();\n",
+ " </script>\n",
+ " </div>\n",
+ "\n",
+ " </div>\n",
+ " </div>\n"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "dataframe",
+ "variable_name": "metrics",
+ "summary": "{\n \"name\": \"metrics\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"epoch\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 0,\n \"max\": 4,\n \"num_unique_values\": 5,\n \"samples\": [\n 1,\n 4,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"step\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 210,\n \"min\": 140,\n \"max\": 704,\n \"num_unique_values\": 5,\n \"samples\": [\n 281,\n 704,\n 422\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"train_MAE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"train_RMSE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"train_Total_Loss\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"val_MAE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"val_RMSE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"val_Total_Loss\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
+ }
+ },
+ "metadata": {},
+ "execution_count": 42
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "i=0\n",
+ "prediction=np.zeros(len(test_data))\n",
+ "for i in range(len(structures_test)):\n",
+ " prediction[i]=model.predict_structure(structures_test[i])"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 233
+ },
+ "id": "XUzf_WX5jZb5",
+ "outputId": "be203ece-03f9-43e1-d66d-3f2213753479"
+ },
+ "execution_count": 43,
+ "outputs": [
+ {
+ "output_type": "error",
+ "ename": "NameError",
+ "evalue": "name 'structures_test' is not defined",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-43-2431dbd08dbf>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstructures_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstructures_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'structures_test' is not defined"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "txs3tg93kQfr"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/Workshop3/.ipynb_checkpoints/molcal-checkpoint.ipynb b/Workshop3/.ipynb_checkpoints/molcal-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a6e031d2e5744017b71d6711507fc5d8368996a3
--- /dev/null
+++ b/Workshop3/.ipynb_checkpoints/molcal-checkpoint.ipynb
@@ -0,0 +1,4004 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
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+ "_model_module": "@jupyter-widgets/controls",
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+ "_view_module_version": "1.5.0",
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+ "IPY_MODEL_79500fbf2f93417796e265786cb54a43",
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+ "IPY_MODEL_c26f6b42e3f348f2a4e161765d99042e"
+ ],
+ "layout": "IPY_MODEL_d7150457f96049c88b5a7e2604670830"
+ }
+ },
+ "79500fbf2f93417796e265786cb54a43": {
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+ "_model_name": "HTMLModel",
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+ "_view_module_version": "1.5.0",
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+ "Collecting jarvis-tools\n",
+ " Downloading jarvis_tools-2024.10.10-py2.py3-none-any.whl.metadata (3.1 kB)\n",
+ "Requirement already satisfied: numpy>=1.20.1 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (1.26.4)\n",
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+ "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (3.8.0)\n",
+ "Requirement already satisfied: joblib>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (1.4.2)\n",
+ "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (2.32.3)\n",
+ "Requirement already satisfied: toolz>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (0.12.1)\n",
+ "Collecting xmltodict>=0.11.0 (from jarvis-tools)\n",
+ " Downloading xmltodict-0.14.2-py2.py3-none-any.whl.metadata (8.0 kB)\n",
+ "Requirement already satisfied: tqdm>=4.41.1 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (4.66.6)\n",
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+ "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (1.3.0)\n",
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+ "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (4.54.1)\n",
+ "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (1.4.7)\n",
+ "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (24.1)\n",
+ "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (10.4.0)\n",
+ "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (3.2.0)\n",
+ "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (2.8.2)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools) (3.4.0)\n",
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+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools) (2.2.3)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools) (2024.8.30)\n",
+ "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->jarvis-tools) (3.5.0)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.0.0->jarvis-tools) (1.16.0)\n",
+ "Downloading jarvis_tools-2024.10.10-py2.py3-none-any.whl (4.2 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading xmltodict-0.14.2-py2.py3-none-any.whl (10.0 kB)\n",
+ "Installing collected packages: xmltodict, jarvis-tools\n",
+ "Successfully installed jarvis-tools-2024.10.10 xmltodict-0.14.2\n"
+ ]
+ }
+ ],
+ "source": [
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+ },
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+ "source": [
+ " !pip3 install pymatgen"
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+ },
+ "execution_count": 2,
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+ "text": [
+ "Collecting pymatgen\n",
+ " Downloading pymatgen-2024.10.29-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
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+ "Collecting monty>=2024.7.29 (from pymatgen)\n",
+ " Downloading monty-2024.10.21-py3-none-any.whl.metadata (3.6 kB)\n",
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+ "Collecting pybtex>=0.24.0 (from pymatgen)\n",
+ " Downloading pybtex-0.24.0-py2.py3-none-any.whl.metadata (2.0 kB)\n",
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+ "Collecting ruamel.yaml>=0.17.0 (from pymatgen)\n",
+ " Downloading ruamel.yaml-0.18.6-py3-none-any.whl.metadata (23 kB)\n",
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+ "\u001b[?25hDownloading latexcodec-3.0.0-py3-none-any.whl (18 kB)\n",
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+ "\u001b[?25hInstalling collected packages: uncertainties, spglib, ruamel.yaml.clib, palettable, latexcodec, ruamel.yaml, pybtex, monty, pymatgen\n",
+ "Successfully installed latexcodec-3.0.0 monty-2024.10.21 palettable-3.3.3 pybtex-0.24.0 pymatgen-2024.10.29 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.12 spglib-2.5.0 uncertainties-3.2.2\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ " !pip install dgl -f https://data.dgl.ai/wheels/torch-2.1/repo.html"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "510ELKTMF9Yu",
+ "outputId": "663be47b-f545-4f7b-ca73-d9cafaba2115"
+ },
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Looking in links: https://data.dgl.ai/wheels/torch-2.1/repo.html\n",
+ "Collecting dgl\n",
+ " Downloading https://data.dgl.ai/wheels/torch-2.1/dgl-2.4.0-cp310-cp310-manylinux1_x86_64.whl (7.8 MB)\n",
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+ "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (1.26.4)\n",
+ "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from dgl) (24.1)\n",
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+ "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (5.9.5)\n",
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+ "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (1.13.1)\n",
+ "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from dgl) (4.66.6)\n",
+ "Collecting torch<=2.4.0 (from dgl)\n",
+ " Downloading torch-2.4.0-cp310-cp310-manylinux1_x86_64.whl.metadata (26 kB)\n",
+ "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->dgl) (0.7.0)\n",
+ "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->dgl) (2.23.4)\n",
+ "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->dgl) (4.12.2)\n",
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+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (2024.8.30)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (3.16.1)\n",
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+ "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch<=2.4.0->dgl)\n",
+ " Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
+ "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch<=2.4.0->dgl)\n",
+ " Downloading nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
+ "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch<=2.4.0->dgl)\n",
+ " Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
+ "Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch<=2.4.0->dgl)\n",
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+ "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch<=2.4.0->dgl)\n",
+ " Downloading nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
+ "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch<=2.4.0->dgl)\n",
+ " Downloading nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
+ "Collecting nvidia-curand-cu12==10.3.2.106 (from torch<=2.4.0->dgl)\n",
+ " Downloading nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
+ "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch<=2.4.0->dgl)\n",
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+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (209.4 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m209.4/209.4 MB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hInstalling collected packages: triton, nvidia-nvtx-cu12, nvidia-nccl-cu12, nvidia-cusparse-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusolver-cu12, nvidia-cudnn-cu12, torch, dgl\n",
+ " Attempting uninstall: nvidia-nccl-cu12\n",
+ " Found existing installation: nvidia-nccl-cu12 2.23.4\n",
+ " Uninstalling nvidia-nccl-cu12-2.23.4:\n",
+ " Successfully uninstalled nvidia-nccl-cu12-2.23.4\n",
+ " Attempting uninstall: nvidia-cusparse-cu12\n",
+ " Found existing installation: nvidia-cusparse-cu12 12.5.4.2\n",
+ " Uninstalling nvidia-cusparse-cu12-12.5.4.2:\n",
+ " Successfully uninstalled nvidia-cusparse-cu12-12.5.4.2\n",
+ " Attempting uninstall: nvidia-curand-cu12\n",
+ " Found existing installation: nvidia-curand-cu12 10.3.7.77\n",
+ " Uninstalling nvidia-curand-cu12-10.3.7.77:\n",
+ " Successfully uninstalled nvidia-curand-cu12-10.3.7.77\n",
+ " Attempting uninstall: nvidia-cufft-cu12\n",
+ " Found existing installation: nvidia-cufft-cu12 11.3.0.4\n",
+ " Uninstalling nvidia-cufft-cu12-11.3.0.4:\n",
+ " Successfully uninstalled nvidia-cufft-cu12-11.3.0.4\n",
+ " Attempting uninstall: nvidia-cuda-runtime-cu12\n",
+ " Found existing installation: nvidia-cuda-runtime-cu12 12.6.77\n",
+ " Uninstalling nvidia-cuda-runtime-cu12-12.6.77:\n",
+ " Successfully uninstalled nvidia-cuda-runtime-cu12-12.6.77\n",
+ " Attempting uninstall: nvidia-cuda-cupti-cu12\n",
+ " Found existing installation: nvidia-cuda-cupti-cu12 12.6.80\n",
+ " Uninstalling nvidia-cuda-cupti-cu12-12.6.80:\n",
+ " Successfully uninstalled nvidia-cuda-cupti-cu12-12.6.80\n",
+ " Attempting uninstall: nvidia-cublas-cu12\n",
+ " Found existing installation: nvidia-cublas-cu12 12.6.3.3\n",
+ " Uninstalling nvidia-cublas-cu12-12.6.3.3:\n",
+ " Successfully uninstalled nvidia-cublas-cu12-12.6.3.3\n",
+ " Attempting uninstall: nvidia-cusolver-cu12\n",
+ " Found existing installation: nvidia-cusolver-cu12 11.7.1.2\n",
+ " Uninstalling nvidia-cusolver-cu12-11.7.1.2:\n",
+ " Successfully uninstalled nvidia-cusolver-cu12-11.7.1.2\n",
+ " Attempting uninstall: nvidia-cudnn-cu12\n",
+ " Found existing installation: nvidia-cudnn-cu12 9.5.1.17\n",
+ " Uninstalling nvidia-cudnn-cu12-9.5.1.17:\n",
+ " Successfully uninstalled nvidia-cudnn-cu12-9.5.1.17\n",
+ " Attempting uninstall: torch\n",
+ " Found existing installation: torch 2.5.0+cu121\n",
+ " Uninstalling torch-2.5.0+cu121:\n",
+ " Successfully uninstalled torch-2.5.0+cu121\n",
+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+ "torchaudio 2.5.0+cu121 requires torch==2.5.0, but you have torch 2.4.0 which is incompatible.\n",
+ "torchvision 0.20.0+cu121 requires torch==2.5.0, but you have torch 2.4.0 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0mSuccessfully installed dgl-2.4.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvtx-cu12-12.1.105 torch-2.4.0 triton-3.0.0\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ " !pip3 install matgl"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "saZDgwpVGAgq",
+ "outputId": "a16f7d96-a80b-4e72-ac6b-c68a9072455b"
+ },
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting matgl\n",
+ " Downloading matgl-1.1.3-py3-none-any.whl.metadata (16 kB)\n",
+ "Collecting ase (from matgl)\n",
+ " Downloading ase-3.23.0-py3-none-any.whl.metadata (3.8 kB)\n",
+ "Requirement already satisfied: dgl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from matgl) (2.4.0)\n",
+ "Requirement already satisfied: pymatgen in /usr/local/lib/python3.10/dist-packages (from matgl) (2024.10.29)\n",
+ "Collecting lightning (from matgl)\n",
+ " Downloading lightning-2.4.0-py3-none-any.whl.metadata (38 kB)\n",
+ "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from matgl) (2.4.0)\n",
+ "Requirement already satisfied: pydantic in /usr/local/lib/python3.10/dist-packages (from matgl) (2.9.2)\n",
+ "Collecting torchdata<0.8.0 (from matgl)\n",
+ " Downloading torchdata-0.7.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
+ "Requirement already satisfied: networkx>=2.1 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (3.4.2)\n",
+ "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (1.26.4)\n",
+ "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (24.1)\n",
+ "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (2.2.2)\n",
+ "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (5.9.5)\n",
+ "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (6.0.2)\n",
+ "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (2.32.3)\n",
+ "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (1.13.1)\n",
+ "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (4.66.6)\n",
+ "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic->matgl) (0.7.0)\n",
+ "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic->matgl) (2.23.4)\n",
+ "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic->matgl) (4.12.2)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (3.16.1)\n",
+ "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (1.13.1)\n",
+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (3.1.4)\n",
+ "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (2024.10.0)\n",
+ "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (9.1.0.70)\n",
+ "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.3.1)\n",
+ "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (11.0.2.54)\n",
+ "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (10.3.2.106)\n",
+ "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (11.4.5.107)\n",
+ "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.0.106)\n",
+ "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (2.20.5)\n",
+ "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.105)\n",
+ "Requirement already satisfied: triton==3.0.0 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (3.0.0)\n",
+ "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->matgl) (12.6.77)\n",
+ "Requirement already satisfied: urllib3>=1.25 in /usr/local/lib/python3.10/dist-packages (from torchdata<0.8.0->matgl) (2.2.3)\n",
+ "Requirement already satisfied: matplotlib>=3.3.4 in /usr/local/lib/python3.10/dist-packages (from ase->matgl) (3.8.0)\n",
+ "Collecting lightning-utilities<2.0,>=0.10.0 (from lightning->matgl)\n",
+ " Downloading lightning_utilities-0.11.8-py3-none-any.whl.metadata (5.2 kB)\n",
+ "Collecting torchmetrics<3.0,>=0.7.0 (from lightning->matgl)\n",
+ " Downloading torchmetrics-1.5.1-py3-none-any.whl.metadata (20 kB)\n",
+ "Collecting pytorch-lightning (from lightning->matgl)\n",
+ " Downloading pytorch_lightning-2.4.0-py3-none-any.whl.metadata (21 kB)\n",
+ "Requirement already satisfied: joblib>=1 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (1.4.2)\n",
+ "Requirement already satisfied: monty>=2024.7.29 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (2024.10.21)\n",
+ "Requirement already satisfied: palettable>=3.3.3 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (3.3.3)\n",
+ "Requirement already satisfied: plotly>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (5.24.1)\n",
+ "Requirement already satisfied: pybtex>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (0.24.0)\n",
+ "Requirement already satisfied: ruamel.yaml>=0.17.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (0.18.6)\n",
+ "Requirement already satisfied: spglib>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (2.5.0)\n",
+ "Requirement already satisfied: tabulate>=0.9 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (0.9.0)\n",
+ "Requirement already satisfied: uncertainties>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (3.2.2)\n",
+ "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (3.10.10)\n",
+ "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from lightning-utilities<2.0,>=0.10.0->lightning->matgl) (75.1.0)\n",
+ "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (1.3.0)\n",
+ "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (0.12.1)\n",
+ "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (4.54.1)\n",
+ "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (1.4.7)\n",
+ "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (10.4.0)\n",
+ "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (3.2.0)\n",
+ "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl>=2.0.0->matgl) (2024.2)\n",
+ "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl>=2.0.0->matgl) (2024.2)\n",
+ "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly>=4.5.0->pymatgen->matgl) (9.0.0)\n",
+ "Requirement already satisfied: latexcodec>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from pybtex>=0.24.0->pymatgen->matgl) (3.0.0)\n",
+ "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from pybtex>=0.24.0->pymatgen->matgl) (1.16.0)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl>=2.0.0->matgl) (3.4.0)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl>=2.0.0->matgl) (3.10)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl>=2.0.0->matgl) (2024.8.30)\n",
+ "Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from ruamel.yaml>=0.17.0->pymatgen->matgl) (0.2.12)\n",
+ "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->matgl) (1.3.0)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->matgl) (3.0.2)\n",
+ "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (2.4.3)\n",
+ "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (1.3.1)\n",
+ "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (24.2.0)\n",
+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (1.5.0)\n",
+ "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (6.1.0)\n",
+ "Requirement already satisfied: yarl<2.0,>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (1.17.0)\n",
+ "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (4.0.3)\n",
+ "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from yarl<2.0,>=1.12.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (0.2.0)\n",
+ "Downloading matgl-1.1.3-py3-none-any.whl (223 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m223.3/223.3 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading torchdata-0.7.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.7 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m4.7/4.7 MB\u001b[0m \u001b[31m25.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading ase-3.23.0-py3-none-any.whl (2.9 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading lightning-2.4.0-py3-none-any.whl (810 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m811.0/811.0 kB\u001b[0m \u001b[31m30.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading lightning_utilities-0.11.8-py3-none-any.whl (26 kB)\n",
+ "Downloading torchmetrics-1.5.1-py3-none-any.whl (890 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m890.6/890.6 kB\u001b[0m \u001b[31m28.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading pytorch_lightning-2.4.0-py3-none-any.whl (815 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m815.2/815.2 kB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hInstalling collected packages: lightning-utilities, ase, torchmetrics, torchdata, pytorch-lightning, lightning, matgl\n",
+ "Successfully installed ase-3.23.0 lightning-2.4.0 lightning-utilities-0.11.8 matgl-1.1.3 pytorch-lightning-2.4.0 torchdata-0.7.1 torchmetrics-1.5.1\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from __future__ import annotations\n",
+ "\n",
+ "import os\n",
+ "import shutil\n",
+ "import warnings\n",
+ "import zipfile\n",
+ "import matgl\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import pytorch_lightning as pl\n",
+ "import torch\n",
+ "import pickle\n",
+ "import numpy as np\n",
+ "from dgl.data.utils import split_dataset\n",
+ "from pymatgen.core import Structure\n",
+ "from pytorch_lightning.loggers import CSVLogger\n",
+ "from lightning.pytorch import Trainer\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "from matgl.ext.pymatgen import Structure2Graph, get_element_list\n",
+ "from matgl.graph.data import MGLDataset, MGLDataLoader #collate_fn. - shivani i don't think you need this as num_workers=0\n",
+ "from matgl.layers import BondExpansion\n",
+ "from matgl.models import MEGNet\n",
+ "from matgl.utils.io import RemoteFile\n",
+ "from matgl.utils.training import ModelLightningModule\n",
+ "\n",
+ "# To suppress warnings for clearer output\n",
+ "warnings.simplefilter(\"ignore\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "uHFBr-yJGC8G",
+ "outputId": "74eed14d-d004-4666-95f5-57d3411d045c"
+ },
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "DGL backend not selected or invalid. Assuming PyTorch for now.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable. Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from jarvis.db.figshare import data\n",
+ "\n",
+ "dft_3d = data('dft_3d')"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "WfEpSLibGHh2",
+ "outputId": "1e900a8f-9920-4d5c-fb25-cd459c23ead3"
+ },
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Obtaining 3D dataset 76k ...\n",
+ "Reference:https://www.nature.com/articles/s41524-020-00440-1\n",
+ "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 40.8M/40.8M [00:02<00:00, 20.0MiB/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Loading the zipfile...\n",
+ "Loading completed.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dft_3d[0].keys()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "2wNH5_F6GM_E",
+ "outputId": "9f14897d-e01c-4f12-f504-e06724d18eb2"
+ },
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "dict_keys(['jid', 'spg_number', 'spg_symbol', 'formula', 'formation_energy_peratom', 'func', 'optb88vdw_bandgap', 'atoms', 'slme', 'magmom_oszicar', 'spillage', 'elastic_tensor', 'effective_masses_300K', 'kpoint_length_unit', 'maxdiff_mesh', 'maxdiff_bz', 'encut', 'optb88vdw_total_energy', 'epsx', 'epsy', 'epsz', 'mepsx', 'mepsy', 'mepsz', 'modes', 'magmom_outcar', 'max_efg', 'avg_elec_mass', 'avg_hole_mass', 'icsd', 'dfpt_piezo_max_eij', 'dfpt_piezo_max_dij', 'dfpt_piezo_max_dielectric', 'dfpt_piezo_max_dielectric_electronic', 'dfpt_piezo_max_dielectric_ionic', 'max_ir_mode', 'min_ir_mode', 'n-Seebeck', 'p-Seebeck', 'n-powerfact', 'p-powerfact', 'ncond', 'pcond', 'nkappa', 'pkappa', 'ehull', 'Tc_supercon', 'dimensionality', 'efg', 'xml_data_link', 'typ', 'exfoliation_energy', 'spg', 'crys', 'density', 'poisson', 'raw_files', 'nat', 'bulk_modulus_kv', 'shear_modulus_gv', 'mbj_bandgap', 'hse_gap', 'reference', 'search'])"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 7
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ " ## Let's make a dataframe from this:\n",
+ "import pandas as pd\n",
+ "import numpy as np"
+ ],
+ "metadata": {
+ "id": "6HEIHJvTGPDs"
+ },
+ "execution_count": 8,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df=pd.DataFrame(dft_3d)\n",
+ "df.head()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 429
+ },
+ "id": "RT5c9__tGRKC",
+ "outputId": "ca6651ee-1d6d-4930-ff8d-aa88babf8e08"
+ },
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " jid spg_number spg_symbol formula formation_energy_peratom \\\n",
+ "0 JVASP-90856 129 P4/nmm TiCuSiAs -0.42762 \n",
+ "1 JVASP-86097 221 Pm-3m DyB6 -0.41596 \n",
+ "2 JVASP-64906 119 I-4m2 Be2OsRu 0.04847 \n",
+ "3 JVASP-98225 14 P2_1/c KBi -0.44140 \n",
+ "4 JVASP-10 164 P-3m1 VSe2 -0.71026 \n",
+ "\n",
+ " func optb88vdw_bandgap \\\n",
+ "0 OptB88vdW 0.000 \n",
+ "1 OptB88vdW 0.000 \n",
+ "2 OptB88vdW 0.000 \n",
+ "3 OptB88vdW 0.472 \n",
+ "4 OptB88vdW 0.000 \n",
+ "\n",
+ " atoms slme magmom_oszicar ... \\\n",
+ "0 {'lattice_mat': [[3.566933224304235, 0.0, -0.0... na 0.0 ... \n",
+ "1 {'lattice_mat': [[4.089078911208881, 0.0, 0.0]... na 0.0 ... \n",
+ "2 {'lattice_mat': [[-1.833590720595598, 1.833590... na 0.0 ... \n",
+ "3 {'lattice_mat': [[7.2963518353359165, 0.0, 0.0... na 0.0 ... \n",
+ "4 {'lattice_mat': [[1.6777483798834445, -2.90594... na 0.0 ... \n",
+ "\n",
+ " density poisson raw_files nat \\\n",
+ "0 5.956 na [] 8 \n",
+ "1 5.522 na [OPT-LOPTICS,JVASP-86097.zip,https://ndownload... 7 \n",
+ "2 10.960 na [OPT-LOPTICS,JVASP-64906.zip,https://ndownload... 4 \n",
+ "3 5.145 na [] 32 \n",
+ "4 5.718 0.23 [FD-ELAST,JVASP-10.zip,https://ndownloader.fig... 3 \n",
+ "\n",
+ " bulk_modulus_kv shear_modulus_gv mbj_bandgap hse_gap \\\n",
+ "0 na na na na \n",
+ "1 na na na na \n",
+ "2 na na na na \n",
+ "3 na na na na \n",
+ "4 48.79 33.05 0.0 na \n",
+ "\n",
+ " reference search \n",
+ "0 mp-1080455 -As-Cu-Si-Ti \n",
+ "1 mp-568319 -B-Dy \n",
+ "2 auid-3eaf68dd483bf4f4 -Be-Os-Ru \n",
+ "3 mp-31104 -Bi-K \n",
+ "4 mp-694 -Se-V \n",
+ "\n",
+ "[5 rows x 64 columns]"
+ ],
+ "text/html": [
+ "\n",
+ " <div id=\"df-9e94c52f-4b6c-4f88-81b3-0d74cccf7eec\" class=\"colab-df-container\">\n",
+ " <div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>jid</th>\n",
+ " <th>spg_number</th>\n",
+ " <th>spg_symbol</th>\n",
+ " <th>formula</th>\n",
+ " <th>formation_energy_peratom</th>\n",
+ " <th>func</th>\n",
+ " <th>optb88vdw_bandgap</th>\n",
+ " <th>atoms</th>\n",
+ " <th>slme</th>\n",
+ " <th>magmom_oszicar</th>\n",
+ " <th>...</th>\n",
+ " <th>density</th>\n",
+ " <th>poisson</th>\n",
+ " <th>raw_files</th>\n",
+ " <th>nat</th>\n",
+ " <th>bulk_modulus_kv</th>\n",
+ " <th>shear_modulus_gv</th>\n",
+ " <th>mbj_bandgap</th>\n",
+ " <th>hse_gap</th>\n",
+ " <th>reference</th>\n",
+ " <th>search</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>JVASP-90856</td>\n",
+ " <td>129</td>\n",
+ " <td>P4/nmm</td>\n",
+ " <td>TiCuSiAs</td>\n",
+ " <td>-0.42762</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[3.566933224304235, 0.0, -0.0...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.956</td>\n",
+ " <td>na</td>\n",
+ " <td>[]</td>\n",
+ " <td>8</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-1080455</td>\n",
+ " <td>-As-Cu-Si-Ti</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>JVASP-86097</td>\n",
+ " <td>221</td>\n",
+ " <td>Pm-3m</td>\n",
+ " <td>DyB6</td>\n",
+ " <td>-0.41596</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[4.089078911208881, 0.0, 0.0]...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.522</td>\n",
+ " <td>na</td>\n",
+ " <td>[OPT-LOPTICS,JVASP-86097.zip,https://ndownload...</td>\n",
+ " <td>7</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-568319</td>\n",
+ " <td>-B-Dy</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>JVASP-64906</td>\n",
+ " <td>119</td>\n",
+ " <td>I-4m2</td>\n",
+ " <td>Be2OsRu</td>\n",
+ " <td>0.04847</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[-1.833590720595598, 1.833590...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>10.960</td>\n",
+ " <td>na</td>\n",
+ " <td>[OPT-LOPTICS,JVASP-64906.zip,https://ndownload...</td>\n",
+ " <td>4</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>auid-3eaf68dd483bf4f4</td>\n",
+ " <td>-Be-Os-Ru</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>JVASP-98225</td>\n",
+ " <td>14</td>\n",
+ " <td>P2_1/c</td>\n",
+ " <td>KBi</td>\n",
+ " <td>-0.44140</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.472</td>\n",
+ " <td>{'lattice_mat': [[7.2963518353359165, 0.0, 0.0...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.145</td>\n",
+ " <td>na</td>\n",
+ " <td>[]</td>\n",
+ " <td>32</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-31104</td>\n",
+ " <td>-Bi-K</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>JVASP-10</td>\n",
+ " <td>164</td>\n",
+ " <td>P-3m1</td>\n",
+ " <td>VSe2</td>\n",
+ " <td>-0.71026</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[1.6777483798834445, -2.90594...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.718</td>\n",
+ " <td>0.23</td>\n",
+ " <td>[FD-ELAST,JVASP-10.zip,https://ndownloader.fig...</td>\n",
+ " <td>3</td>\n",
+ " <td>48.79</td>\n",
+ " <td>33.05</td>\n",
+ " <td>0.0</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-694</td>\n",
+ " <td>-Se-V</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 64 columns</p>\n",
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+ " border-right-color: var(--fill-color);\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " }\n",
+ " 90% {\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " }\n",
+ " }\n",
+ "</style>\n",
+ "\n",
+ " <script>\n",
+ " async function quickchart(key) {\n",
+ " const quickchartButtonEl =\n",
+ " document.querySelector('#' + key + ' button');\n",
+ " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
+ " quickchartButtonEl.classList.add('colab-df-spinner');\n",
+ " try {\n",
+ " const charts = await google.colab.kernel.invokeFunction(\n",
+ " 'suggestCharts', [key], {});\n",
+ " } catch (error) {\n",
+ " console.error('Error during call to suggestCharts:', error);\n",
+ " }\n",
+ " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+ " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+ " }\n",
+ " (() => {\n",
+ " let quickchartButtonEl =\n",
+ " document.querySelector('#df-66c438ee-b30e-48e1-808e-7b2da6583b0f button');\n",
+ " quickchartButtonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ " })();\n",
+ " </script>\n",
+ "</div>\n",
+ "\n",
+ " </div>\n",
+ " </div>\n"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "dataframe",
+ "variable_name": "df"
+ }
+ },
+ "metadata": {},
+ "execution_count": 9
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "## Count number of entries for each property\n",
+ "for i in df.columns.values:\n",
+ " val=df[i].replace('na',np.nan).dropna().values\n",
+ " print(i,len(val))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "FTsZSTDbGUbH",
+ "outputId": "3a024410-29ed-442a-c405-a90b5985da09"
+ },
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "jid 75993\n",
+ "spg_number 75993\n",
+ "spg_symbol 75993\n",
+ "formula 75993\n",
+ "formation_energy_peratom 75993\n",
+ "func 75993\n",
+ "optb88vdw_bandgap 75993\n",
+ "atoms 75993\n",
+ "slme 9770\n",
+ "magmom_oszicar 71320\n",
+ "spillage 11377\n",
+ "elastic_tensor 25513\n",
+ "effective_masses_300K 75993\n",
+ "kpoint_length_unit 75671\n",
+ "maxdiff_mesh 5861\n",
+ "maxdiff_bz 5861\n",
+ "encut 75670\n",
+ "optb88vdw_total_energy 75993\n",
+ "epsx 52168\n",
+ "epsy 52168\n",
+ "epsz 52168\n",
+ "mepsx 18293\n",
+ "mepsy 18293\n",
+ "mepsz 18293\n",
+ "modes 13910\n",
+ "magmom_outcar 74261\n",
+ "max_efg 11871\n",
+ "avg_elec_mass 17645\n",
+ "avg_hole_mass 17645\n",
+ "icsd 75993\n",
+ "dfpt_piezo_max_eij 4799\n",
+ "dfpt_piezo_max_dij 3347\n",
+ "dfpt_piezo_max_dielectric 4706\n",
+ "dfpt_piezo_max_dielectric_electronic 4809\n",
+ "dfpt_piezo_max_dielectric_ionic 4809\n",
+ "max_ir_mode 4805\n",
+ "min_ir_mode 4809\n",
+ "n-Seebeck 23218\n",
+ "p-Seebeck 23218\n",
+ "n-powerfact 23218\n",
+ "p-powerfact 23218\n",
+ "ncond 23218\n",
+ "pcond 23218\n",
+ "nkappa 23218\n",
+ "pkappa 23218\n",
+ "ehull 75993\n",
+ "Tc_supercon 1058\n",
+ "dimensionality 75560\n",
+ "efg 75993\n",
+ "xml_data_link 75993\n",
+ "typ 75993\n",
+ "exfoliation_energy 813\n",
+ "spg 75993\n",
+ "crys 75993\n",
+ "density 75993\n",
+ "poisson 23597\n",
+ "raw_files 75993\n",
+ "nat 75993\n",
+ "bulk_modulus_kv 23824\n",
+ "shear_modulus_gv 23824\n",
+ "mbj_bandgap 19805\n",
+ "hse_gap 56\n",
+ "reference 75993\n",
+ "search 75993\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from jarvis.core.atoms import Atoms\n",
+ "bm=df[df.bulk_modulus_kv != 'na']\n",
+ "data = [(Atoms.from_dict(bm.iloc[i]['atoms']).pymatgen_converter(), bm.iloc[i].bulk_modulus_kv) for i in range(len(bm))]"
+ ],
+ "metadata": {
+ "id": "rW4KEnICGVxE"
+ },
+ "execution_count": 11,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import itertools\n",
+ "def get_stoichiometry(elements):\n",
+ " return [(g[0], len(list(g[1]))) for g in itertools.groupby(elements)]"
+ ],
+ "metadata": {
+ "id": "0gQUS5rQGaK5"
+ },
+ "execution_count": 12,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ " ## Use all the material dataset for training the bulk modulus\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "stoichs=[] #stoichiometry\n",
+ "bulk=[] #only include positive bulk modulus\n",
+ "\n",
+ "for i in tqdm(range(len(bm))):\n",
+ " if (bm.iloc[i]['bulk_modulus_kv'])>1:\n",
+ " stoichs.append(Atoms.from_dict(bm.iloc[i]['atoms']).pymatgen_converter())\n",
+ " bulk.append(bm.iloc[i]['bulk_modulus_kv'])\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "YaMVVrguGe2s",
+ "outputId": "25dcd209-e980-4a20-c650-eb17046dda62"
+ },
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 23824/23824 [00:36<00:00, 656.64it/s] \n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "data_ran=list(zip(stoichs,bulk))\n",
+ "#write out the dataset, to train later\n",
+ "import pickle\n",
+ "with open('data_ran.pickle', 'wb') as f:\n",
+ " pickle.dump(data_ran, f)"
+ ],
+ "metadata": {
+ "id": "0lxmfaHEHhs6"
+ },
+ "execution_count": 18,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ " #read in the dataset\n",
+ "data_ran=pd.read_pickle('./data_ran.pickle')"
+ ],
+ "metadata": {
+ "id": "dz2y9oNGH-rO"
+ },
+ "execution_count": 19,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import random\n",
+ "\n",
+ "random.shuffle(data_ran)\n",
+ "\n",
+ "structures=[d[0] for d in data_ran]\n",
+ "targets=np.log10([d[1] for d in data_ran])\n",
+ "\n",
+ "print(structures[0],targets[0])"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "IllZhvMxHh0x",
+ "outputId": "9d8e5d95-ff3b-4b06-d674-ac15c663fe6e"
+ },
+ "execution_count": 20,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Full Formula (Al2 O1)\n",
+ "Reduced Formula: Al2O\n",
+ "abc : 2.691974 2.821216 5.955222\n",
+ "angles: 90.000000 90.000000 90.000000\n",
+ "pbc : True True True\n",
+ "Sites (3)\n",
+ " # SP a b c\n",
+ "--- ---- --------- --- --------\n",
+ " 0 Al -0.033312 0 0.757299\n",
+ " 1 Al -0.033312 0 0.242701\n",
+ " 2 O 0.466623 0 0 1.757547853469244\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# get element types in the dataset\n",
+ "elem_list = get_element_list(structures)\n",
+ "# setup a graph converter\n",
+ "converter = Structure2Graph(element_types=elem_list, cutoff=4.0)\n",
+ "# convert the raw dataset into MEGNetDataset\n",
+ "mp_dataset = MGLDataset(\n",
+ " structures=structures,\n",
+ " labels={\"bulk_modulus_kv\": targets},\n",
+ " converter=converter,\n",
+ ")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gkBUklhmIeOb",
+ "outputId": "13cc0804-8a6b-4a18-9153-228fe781ab5b"
+ },
+ "execution_count": 21,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 23173/23173 [00:47<00:00, 483.02it/s]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ " train_data, val_data, test_data = split_dataset(\n",
+ " mp_dataset,\n",
+ " frac_list=[0.6, 0.2, 0.2],\n",
+ " shuffle=True,\n",
+ " random_state=42,\n",
+ ")\n",
+ "train_loader, val_loader, test_loader = MGLDataLoader(\n",
+ " train_data=train_data,\n",
+ " val_data=val_data,\n",
+ " test_data=test_data,\n",
+ " batch_size=64,\n",
+ " num_workers=0,\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "Tk7mjkwDIqOJ"
+ },
+ "execution_count": 22,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# setup the embedding layer for node attributes\n",
+ "node_embed = torch.nn.Embedding(len(elem_list), 16)\n",
+ "# define the bond expansion\n",
+ "bond_expansion = BondExpansion(rbf_type=\"Gaussian\", initial=0.0, final=5.0, num_centers=100, width=0.5)\n",
+ "\n",
+ "# setup the architecture of MEGNet model\n",
+ "model = MEGNet(\n",
+ " dim_node_embedding=16,\n",
+ " dim_edge_embedding=100,\n",
+ " dim_state_embedding=2,\n",
+ " nblocks=3,\n",
+ " hidden_layer_sizes_input=(64, 32),\n",
+ " hidden_layer_sizes_conv=(64, 64, 32),\n",
+ " nlayers_set2set=1,\n",
+ " niters_set2set=2,\n",
+ " hidden_layer_sizes_output=(32, 16),\n",
+ " is_classification=False,\n",
+ " activation_type=\"softplus2\",\n",
+ " bond_expansion=bond_expansion,\n",
+ " #collate_fn=collate_fn, shivani - not needed now?\n",
+ " cutoff=4.0,\n",
+ " gauss_width=0.5,\n",
+ ")\n",
+ "\n",
+ "# setup the MEGNetTrainer\n",
+ "lit_module = ModelLightningModule(model=model)"
+ ],
+ "metadata": {
+ "id": "jREU_HYVIvoG"
+ },
+ "execution_count": 23,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "logger = CSVLogger(\"logged\", name=\"MEGNet_training\")\n",
+ "trainer = Trainer(max_epochs=5, accelerator=\"cpu\", logger=logger) #set to SMALL NUMBER FOR TESTING, PLEASE CHANGE.\n",
+ "trainer.fit(model=lit_module, train_dataloaders=train_loader, val_dataloaders=val_loader)\n",
+ "\n",
+ "warnings.simplefilter(\"ignore\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 656,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "r-fFV2ncI-zW",
+ "outputId": "758b858a-daf7-42fc-8622-d54f67818163"
+ },
+ "execution_count": 24,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "INFO: GPU available: False, used: False\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n",
+ "INFO: TPU available: False, using: 0 TPU cores\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
+ "INFO: HPU available: False, using: 0 HPUs\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
+ "INFO: \n",
+ " | Name | Type | Params | Mode \n",
+ "----------------------------------------------------\n",
+ "0 | model | MEGNet | 189 K | train\n",
+ "1 | mae | MeanAbsoluteError | 0 | train\n",
+ "2 | rmse | MeanSquaredError | 0 | train\n",
+ "----------------------------------------------------\n",
+ "189 K Trainable params\n",
+ "100 Non-trainable params\n",
+ "189 K Total params\n",
+ "0.758 Total estimated model params size (MB)\n",
+ "109 Modules in train mode\n",
+ "0 Modules in eval mode\n",
+ "INFO:lightning.pytorch.callbacks.model_summary:\n",
+ " | Name | Type | Params | Mode \n",
+ "----------------------------------------------------\n",
+ "0 | model | MEGNet | 189 K | train\n",
+ "1 | mae | MeanAbsoluteError | 0 | train\n",
+ "2 | rmse | MeanSquaredError | 0 | train\n",
+ "----------------------------------------------------\n",
+ "189 K Trainable params\n",
+ "100 Non-trainable params\n",
+ "189 K Total params\n",
+ "0.758 Total estimated model params size (MB)\n",
+ "109 Modules in train mode\n",
+ "0 Modules in eval mode\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Sanity Checking: | | 0/? [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "f7321d7b1b9a4dbe905092f388240f23"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Training: | | 0/? [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "56705796817d43028c6eddfefc18336f"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "73653ee65cde4122ba0268dabc34f827"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d79c6b456ffb4289921288199c1a5e93"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "3b8db07a150a4f058a540e5d584a981c"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "8f22afc4e41a45f28fb1c81f37c554fc"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "21db824539da4758a8aa5c6351949cc5"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "INFO: `Trainer.fit` stopped: `max_epochs=5` reached.\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=5` reached.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "metrics = pd.read_csv(\"logged/MEGNet_training/version_0/metrics.csv\")\n",
+ "metrics[\"train_MAE\"].dropna().plot()\n",
+ "metrics[\"val_MAE\"].dropna().plot()\n",
+ "\n",
+ "_ = plt.legend()\n",
+ "#plt.savefig(\"loss.jpg\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 430
+ },
+ "id": "JpFwUt4_JMjZ",
+ "outputId": "06c0bfc7-7c4b-40fa-b4d6-35e054a407bc"
+ },
+ "execution_count": 25,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ]
+ }
+ ]
+}
diff --git a/Workshop3/.ipynb_checkpoints/molcal_workshop3-checkpoint.ipynb b/Workshop3/.ipynb_checkpoints/molcal_workshop3-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4228bc38304f60847328921ff68667d019cf3850
--- /dev/null
+++ b/Workshop3/.ipynb_checkpoints/molcal_workshop3-checkpoint.ipynb
@@ -0,0 +1,982 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!pip install jarvis-tools"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Obtaining 3D dataset 76k ...\n",
+ "Reference:https://www.nature.com/articles/s41524-020-00440-1\n",
+ "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n",
+ "Loading the zipfile...\n",
+ "Loading completed.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# !pip install jarvis-tools, and restart the notebook\n",
+ "from jarvis.db.figshare import data\n",
+ "\n",
+ "dft_3d = data('dft_3d')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['jid', 'spg_number', 'spg_symbol', 'formula', 'formation_energy_peratom', 'func', 'optb88vdw_bandgap', 'atoms', 'slme', 'magmom_oszicar', 'spillage', 'elastic_tensor', 'effective_masses_300K', 'kpoint_length_unit', 'maxdiff_mesh', 'maxdiff_bz', 'encut', 'optb88vdw_total_energy', 'epsx', 'epsy', 'epsz', 'mepsx', 'mepsy', 'mepsz', 'modes', 'magmom_outcar', 'max_efg', 'avg_elec_mass', 'avg_hole_mass', 'icsd', 'dfpt_piezo_max_eij', 'dfpt_piezo_max_dij', 'dfpt_piezo_max_dielectric', 'dfpt_piezo_max_dielectric_electronic', 'dfpt_piezo_max_dielectric_ionic', 'max_ir_mode', 'min_ir_mode', 'n-Seebeck', 'p-Seebeck', 'n-powerfact', 'p-powerfact', 'ncond', 'pcond', 'nkappa', 'pkappa', 'ehull', 'Tc_supercon', 'dimensionality', 'efg', 'xml_data_link', 'typ', 'exfoliation_energy', 'spg', 'crys', 'density', 'poisson', 'raw_files', 'nat', 'bulk_modulus_kv', 'shear_modulus_gv', 'mbj_bandgap', 'hse_gap', 'reference', 'search'])"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dft_3d[0].keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Let's make a dataframe from this:\n",
+ "## !pip install pandas ## if it's not installed\n",
+ "import pandas as pd\n",
+ "import numpy as np "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df=pd.DataFrame(dft_3d)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>jid</th>\n",
+ " <th>spg_number</th>\n",
+ " <th>spg_symbol</th>\n",
+ " <th>formula</th>\n",
+ " <th>formation_energy_peratom</th>\n",
+ " <th>func</th>\n",
+ " <th>optb88vdw_bandgap</th>\n",
+ " <th>atoms</th>\n",
+ " <th>slme</th>\n",
+ " <th>magmom_oszicar</th>\n",
+ " <th>...</th>\n",
+ " <th>density</th>\n",
+ " <th>poisson</th>\n",
+ " <th>raw_files</th>\n",
+ " <th>nat</th>\n",
+ " <th>bulk_modulus_kv</th>\n",
+ " <th>shear_modulus_gv</th>\n",
+ " <th>mbj_bandgap</th>\n",
+ " <th>hse_gap</th>\n",
+ " <th>reference</th>\n",
+ " <th>search</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>JVASP-90856</td>\n",
+ " <td>129</td>\n",
+ " <td>P4/nmm</td>\n",
+ " <td>TiCuSiAs</td>\n",
+ " <td>-0.42762</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[3.566933224304235, 0.0, -0.0...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.956</td>\n",
+ " <td>na</td>\n",
+ " <td>[]</td>\n",
+ " <td>8</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-1080455</td>\n",
+ " <td>-As-Cu-Si-Ti</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>JVASP-86097</td>\n",
+ " <td>221</td>\n",
+ " <td>Pm-3m</td>\n",
+ " <td>DyB6</td>\n",
+ " <td>-0.41596</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[4.089078911208881, 0.0, 0.0]...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.522</td>\n",
+ " <td>na</td>\n",
+ " <td>[OPT-LOPTICS,JVASP-86097.zip,https://ndownload...</td>\n",
+ " <td>7</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-568319</td>\n",
+ " <td>-B-Dy</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>JVASP-64906</td>\n",
+ " <td>119</td>\n",
+ " <td>I-4m2</td>\n",
+ " <td>Be2OsRu</td>\n",
+ " <td>0.04847</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[-1.833590720595598, 1.833590...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>10.960</td>\n",
+ " <td>na</td>\n",
+ " <td>[OPT-LOPTICS,JVASP-64906.zip,https://ndownload...</td>\n",
+ " <td>4</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>auid-3eaf68dd483bf4f4</td>\n",
+ " <td>-Be-Os-Ru</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>JVASP-98225</td>\n",
+ " <td>14</td>\n",
+ " <td>P2_1/c</td>\n",
+ " <td>KBi</td>\n",
+ " <td>-0.44140</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.472</td>\n",
+ " <td>{'lattice_mat': [[7.2963518353359165, 0.0, 0.0...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.145</td>\n",
+ " <td>na</td>\n",
+ " <td>[]</td>\n",
+ " <td>32</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-31104</td>\n",
+ " <td>-Bi-K</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>JVASP-10</td>\n",
+ " <td>164</td>\n",
+ " <td>P-3m1</td>\n",
+ " <td>VSe2</td>\n",
+ " <td>-0.71026</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[1.6777483798834445, -2.90594...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.718</td>\n",
+ " <td>0.23</td>\n",
+ " <td>[FD-ELAST,JVASP-10.zip,https://ndownloader.fig...</td>\n",
+ " <td>3</td>\n",
+ " <td>48.79</td>\n",
+ " <td>33.05</td>\n",
+ " <td>0.0</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-694</td>\n",
+ " <td>-Se-V</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 64 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " jid spg_number spg_symbol formula formation_energy_peratom \\\n",
+ "0 JVASP-90856 129 P4/nmm TiCuSiAs -0.42762 \n",
+ "1 JVASP-86097 221 Pm-3m DyB6 -0.41596 \n",
+ "2 JVASP-64906 119 I-4m2 Be2OsRu 0.04847 \n",
+ "3 JVASP-98225 14 P2_1/c KBi -0.44140 \n",
+ "4 JVASP-10 164 P-3m1 VSe2 -0.71026 \n",
+ "\n",
+ " func optb88vdw_bandgap \\\n",
+ "0 OptB88vdW 0.000 \n",
+ "1 OptB88vdW 0.000 \n",
+ "2 OptB88vdW 0.000 \n",
+ "3 OptB88vdW 0.472 \n",
+ "4 OptB88vdW 0.000 \n",
+ "\n",
+ " atoms slme magmom_oszicar ... \\\n",
+ "0 {'lattice_mat': [[3.566933224304235, 0.0, -0.0... na 0.0 ... \n",
+ "1 {'lattice_mat': [[4.089078911208881, 0.0, 0.0]... na 0.0 ... \n",
+ "2 {'lattice_mat': [[-1.833590720595598, 1.833590... na 0.0 ... \n",
+ "3 {'lattice_mat': [[7.2963518353359165, 0.0, 0.0... na 0.0 ... \n",
+ "4 {'lattice_mat': [[1.6777483798834445, -2.90594... na 0.0 ... \n",
+ "\n",
+ " density poisson raw_files nat \\\n",
+ "0 5.956 na [] 8 \n",
+ "1 5.522 na [OPT-LOPTICS,JVASP-86097.zip,https://ndownload... 7 \n",
+ "2 10.960 na [OPT-LOPTICS,JVASP-64906.zip,https://ndownload... 4 \n",
+ "3 5.145 na [] 32 \n",
+ "4 5.718 0.23 [FD-ELAST,JVASP-10.zip,https://ndownloader.fig... 3 \n",
+ "\n",
+ " bulk_modulus_kv shear_modulus_gv mbj_bandgap hse_gap \\\n",
+ "0 na na na na \n",
+ "1 na na na na \n",
+ "2 na na na na \n",
+ "3 na na na na \n",
+ "4 48.79 33.05 0.0 na \n",
+ "\n",
+ " reference search \n",
+ "0 mp-1080455 -As-Cu-Si-Ti \n",
+ "1 mp-568319 -B-Dy \n",
+ "2 auid-3eaf68dd483bf4f4 -Be-Os-Ru \n",
+ "3 mp-31104 -Bi-K \n",
+ "4 mp-694 -Se-V \n",
+ "\n",
+ "[5 rows x 64 columns]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "jid 75993\n",
+ "spg_number 75993\n",
+ "spg_symbol 75993\n",
+ "formula 75993\n",
+ "formation_energy_peratom 75993\n",
+ "func 75993\n",
+ "optb88vdw_bandgap 75993\n",
+ "atoms 75993\n",
+ "slme 9770\n",
+ "magmom_oszicar 71320\n",
+ "spillage 11377\n",
+ "elastic_tensor 25513\n",
+ "effective_masses_300K 75993\n",
+ "kpoint_length_unit 75671\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/p4/gl5hwwk13vjb1pncdz82sq4h0000gq/T/ipykernel_73354/3773379141.py:3: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+ " val=df[i].replace('na',np.nan).dropna().values\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "maxdiff_mesh 5861\n",
+ "maxdiff_bz 5861\n",
+ "encut 75670\n",
+ "optb88vdw_total_energy 75993\n",
+ "epsx 52168\n",
+ "epsy 52168\n",
+ "epsz 52168\n",
+ "mepsx 18293\n",
+ "mepsy 18293\n",
+ "mepsz 18293\n",
+ "modes 13910\n",
+ "magmom_outcar 74261\n",
+ "max_efg 11871\n",
+ "avg_elec_mass 17645\n",
+ "avg_hole_mass 17645\n",
+ "icsd 75993\n",
+ "dfpt_piezo_max_eij 4799\n",
+ "dfpt_piezo_max_dij 3347\n",
+ "dfpt_piezo_max_dielectric 4706\n",
+ "dfpt_piezo_max_dielectric_electronic 4809\n",
+ "dfpt_piezo_max_dielectric_ionic 4809\n",
+ "max_ir_mode 4805\n",
+ "min_ir_mode 4809\n",
+ "n-Seebeck 23218\n",
+ "p-Seebeck 23218\n",
+ "n-powerfact 23218\n",
+ "p-powerfact 23218\n",
+ "ncond 23218\n",
+ "pcond 23218\n",
+ "nkappa 23218\n",
+ "pkappa 23218\n",
+ "ehull 75993\n",
+ "Tc_supercon 1058\n",
+ "dimensionality 75560\n",
+ "efg 75993\n",
+ "xml_data_link 75993\n",
+ "typ 75993\n",
+ "exfoliation_energy 813\n",
+ "spg 75993\n",
+ "crys 75993\n",
+ "density 75993\n",
+ "poisson 23597\n",
+ "raw_files 75993\n",
+ "nat 75993\n",
+ "bulk_modulus_kv 23824\n",
+ "shear_modulus_gv 23824\n",
+ "mbj_bandgap 19805\n",
+ "hse_gap 56\n",
+ "reference 75993\n",
+ "search 75993\n"
+ ]
+ }
+ ],
+ "source": [
+ "## Count number of entries for each property\n",
+ "for i in df.columns.values:\n",
+ " val=df[i].replace('na',np.nan).dropna().values\n",
+ " print(i,len(val))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Filter dataset based on desired property \n",
+ "## We will focus on elastic properties for today, i.e. Bulk modulus"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!pip install pymatgen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from jarvis.core.atoms import Atoms\n",
+ "bm=df[df.bulk_modulus_kv != 'na']\n",
+ "data = [(Atoms.from_dict(bm.iloc[i]['atoms']).pymatgen_converter(), bm.iloc[i].bulk_modulus_kv) for i in range(len(bm))]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import itertools\n",
+ "def get_stoichiometry(elements):\n",
+ " return [(g[0], len(list(g[1]))) for g in itertools.groupby(elements)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 23824/23824 [00:02<00:00, 8443.33it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "## Use all the material dataset for training the bulk modulus\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "stoichs=[] #stoichiometry\n",
+ "bulk=[] #bulk modulus\n",
+ "for i in tqdm(range(len(bm))):\n",
+ " stoichs.append(Atoms.from_dict(bm.iloc[i]['atoms']).pymatgen_converter())\n",
+ " bulk.append(bm.iloc[i]['bulk_modulus_kv'])\n",
+ "data_ran=list(zip(stoichs,bulk))\n",
+ "\n",
+ "import pickle\n",
+ "with open('data_ran.pickle', 'wb') as f:\n",
+ " pickle.dump(data_ran, f)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "### change environments from jarvis to matgl"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/sgrover/anaconda3/envs/matgl-megnet/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "from __future__ import annotations\n",
+ "\n",
+ "import os\n",
+ "import shutil\n",
+ "import warnings\n",
+ "import zipfile\n",
+ "import matgl\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import pytorch_lightning as pl\n",
+ "import torch\n",
+ "import pickle\n",
+ "import numpy as np\n",
+ "from dgl.data.utils import split_dataset\n",
+ "from pymatgen.core import Structure\n",
+ "from pytorch_lightning.loggers import CSVLogger\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "from matgl.ext.pymatgen import Structure2Graph, get_element_list\n",
+ "from matgl.graph.data import MGLDataset, MGLDataLoader, collate_fn\n",
+ "from matgl.layers import BondExpansion\n",
+ "from matgl.models import MEGNet\n",
+ "from matgl.utils.io import RemoteFile\n",
+ "from matgl.utils.training import ModelLightningModule\n",
+ "\n",
+ "# To suppress warnings for clearer output\n",
+ "warnings.simplefilter(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_ran=pd.read_pickle('./data_ran.pickle')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "list"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(data_ran)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import random\n",
+ "random.shuffle(data_ran)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Full Formula (Al2 Cr2 Ge2)\n",
+ "Reduced Formula: AlCrGe\n",
+ "abc : 5.953030 4.915863 4.763705\n",
+ "angles: 75.887124 53.211418 50.901458\n",
+ "pbc : True True True\n",
+ "Sites (6)\n",
+ " # SP a b c\n",
+ "--- ---- -------- -------- --------\n",
+ " 0 Al 0.07707 0.42293 0.07707\n",
+ " 1 Al 0.42293 0.07707 0.42293\n",
+ " 2 Cr 0.75 0.75 0.75\n",
+ " 3 Cr 0 0 0\n",
+ " 4 Ge 0.669987 0.330013 0.669987\n",
+ " 5 Ge 0.330013 0.669987 0.330013 2.1178676265660163\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "structures=[d[0] for d in data_ran[:15000]]\n",
+ "targets=np.log10([d[1] for d in data_ran])\n",
+ "\n",
+ "print(structures[0],targets[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# get element types in the dataset\n",
+ "elem_list = get_element_list(structures)\n",
+ "# setup a graph converter\n",
+ "converter = Structure2Graph(element_types=elem_list, cutoff=4.0)\n",
+ "# convert the raw dataset into MEGNetDataset\n",
+ "mp_dataset = MGLDataset(\n",
+ " structures=structures,\n",
+ " labels={\"bulk_modulus_kv\": targets},\n",
+ " converter=converter,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_data, val_data, test_data = split_dataset(\n",
+ " mp_dataset,\n",
+ " frac_list=[0.8, 0.1, 0.1],\n",
+ " shuffle=True,\n",
+ " random_state=42,\n",
+ ")\n",
+ "train_loader, val_loader, test_loader = MGLDataLoader(\n",
+ " train_data=train_data,\n",
+ " val_data=val_data,\n",
+ " test_data=test_data,\n",
+ " collate_fn=collate_fn,\n",
+ " batch_size=64,\n",
+ " num_workers=0,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# setup the embedding layer for node attributes\n",
+ "node_embed = torch.nn.Embedding(len(elem_list), 16)\n",
+ "# define the bond expansion\n",
+ "bond_expansion = BondExpansion(rbf_type=\"Gaussian\", initial=0.0, final=5.0, num_centers=100, width=0.5)\n",
+ "\n",
+ "# setup the architecture of MEGNet model\n",
+ "model = MEGNet(\n",
+ " dim_node_embedding=16,\n",
+ " dim_edge_embedding=100,\n",
+ " dim_state_embedding=2,\n",
+ " nblocks=3,\n",
+ " hidden_layer_sizes_input=(64, 32),\n",
+ " hidden_layer_sizes_conv=(64, 64, 32),\n",
+ " nlayers_set2set=1,\n",
+ " niters_set2set=2,\n",
+ " hidden_layer_sizes_output=(32, 16),\n",
+ " is_classification=False,\n",
+ " activation_type=\"softplus2\",\n",
+ " bond_expansion=bond_expansion,\n",
+ " cutoff=4.0,\n",
+ " gauss_width=0.5,\n",
+ ")\n",
+ "\n",
+ "# setup the MEGNetTrainer\n",
+ "lit_module = ModelLightningModule(model=model)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True (mps), used: False\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "IPU available: False, using: 0 IPUs\n",
+ "HPU available: False, using: 0 HPUs\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " | Name | Type | Params\n",
+ "--------------------------------------------\n",
+ "0 | model | MEGNet | 189 K \n",
+ "1 | mae | MeanAbsoluteError | 0 \n",
+ "2 | rmse | MeanSquaredError | 0 \n",
+ "--------------------------------------------\n",
+ "189 K Trainable params\n",
+ "100 Non-trainable params\n",
+ "189 K Total params\n",
+ "0.758 Total estimated model params size (MB)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 39: 100%|██████████| 298/298 [00:24<00:00, 11.99it/s, v_num=6, val_Total_Loss=nan.0, val_MAE=nan.0, val_RMSE=nan.0, train_Total_Loss=nan.0, train_MAE=nan.0, train_RMSE=nan.0]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "`Trainer.fit` stopped: `max_epochs=40` reached.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 39: 100%|██████████| 298/298 [00:24<00:00, 11.97it/s, v_num=6, val_Total_Loss=nan.0, val_MAE=nan.0, val_RMSE=nan.0, train_Total_Loss=nan.0, train_MAE=nan.0, train_RMSE=nan.0]\n"
+ ]
+ }
+ ],
+ "source": [
+ "logger = CSVLogger(\"logs\", name=\"MEGNet_training\")\n",
+ "trainer = pl.Trainer(max_epochs=40, accelerator=\"cpu\", logger=logger)\n",
+ "trainer.fit(model=lit_module, train_dataloaders=train_loader, val_dataloaders=val_loader)\n",
+ "\n",
+ "warnings.simplefilter(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "metrics = pd.read_csv(\"logs/MEGNet_training/version_0/metrics.csv\")\n",
+ "metrics[\"train_MAE\"].dropna().plot()\n",
+ "metrics[\"val_MAE\"].dropna().plot()\n",
+ "\n",
+ "_ = plt.legend()\n",
+ "#plt.savefig(\"loss.jpg\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>epoch</th>\n",
+ " <th>step</th>\n",
+ " <th>train_MAE</th>\n",
+ " <th>train_RMSE</th>\n",
+ " <th>train_Total_Loss</th>\n",
+ " <th>val_MAE</th>\n",
+ " <th>val_RMSE</th>\n",
+ " <th>val_Total_Loss</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>297</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0</td>\n",
+ " <td>297</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>1</td>\n",
+ " <td>595</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1</td>\n",
+ " <td>595</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2</td>\n",
+ " <td>893</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>2</td>\n",
+ " <td>893</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>3</td>\n",
+ " <td>1191</td>\n",
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+ " <td>NaN</td>\n",
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+ }
+ ],
+ "source": [
+ "metrics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "i=0\n",
+ "prediction=np.zeros(len(test_data))\n",
+ "for i in range(len(structures_test)):\n",
+ " prediction[i]=model.predict_structure(structures_test[i])"
+ ]
+ }
+ ],
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+ "kernelspec": {
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diff --git a/Workshop3/.ipynb_checkpoints/outline-checkpoint.md b/Workshop3/.ipynb_checkpoints/outline-checkpoint.md
new file mode 100644
index 0000000000000000000000000000000000000000..82be33f7f143527206d90716a1890cd539c3542d
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+++ b/Workshop3/.ipynb_checkpoints/outline-checkpoint.md
@@ -0,0 +1,32 @@
+# Material graph neural networks for materials properties
+
+- Atoms molecules, crystals to graph format
+- State attributes, atoms, bonds
+- Use info to set up the model
+
+## Training NN to get desired material properties
+- Caloric materials:
+- Mechanical properties
+- Vibrational properties
+
+## Training for elastic constant
+- Bulk modulus
+- Start with the dataset (what dataset) to graph network
+
+## Set up training
+- Training, test, validation
+
+## Interested in? Perovskites, plastic crystals, etc.
+
+- How better is the prediction?
+- Compare to the actual values
+
+- change from full materials project to a large subset to a small subset
+- see errors on predictions
+
+## Tuned by changing the dimensionality of the system
+- Efficiency vs accuracy
+- Cpus vs gpus
+
+## Optimisation methods to predict properties where there is less data
+- How can we predict?
diff --git a/Workshop3/MOLCAL3_CLH_081124.pptx b/Workshop3/MOLCAL3_CLH_081124.pptx
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index 0000000000000000000000000000000000000000..1c26fe28a30320507bba59a10134c816a6c275fa
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diff --git a/Workshop3/MolCal3_ppt.pptx b/Workshop3/MolCal3_ppt.pptx
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diff --git a/Workshop3/Molcal_workshop3_materials_property_prediction.ipynb b/Workshop3/Molcal_workshop3_materials_property_prediction.ipynb
index 75035f45ffb9d84f18a91ebdfe0a0d08cf9d13bc..2bfd3cbc5e50097c3737aa6a0b716bfa32786546 100644
--- a/Workshop3/Molcal_workshop3_materials_property_prediction.ipynb
+++ b/Workshop3/Molcal_workshop3_materials_property_prediction.ipynb
@@ -1,4416 +1,4426 @@
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+ "id": "mC4gkbnBeP7t",
+ "outputId": "e8b7d83a-2c04-4909-e84d-1bdf75b8f42d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: pymatgen in /usr/local/lib/python3.10/dist-packages (2024.10.29)\n",
+ "Requirement already satisfied: joblib>=1 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (1.4.2)\n",
+ "Requirement already satisfied: matplotlib>=3.8 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (3.8.0)\n",
+ "Requirement already satisfied: monty>=2024.7.29 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (2024.10.21)\n",
+ "Requirement already satisfied: networkx>=3 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (3.4.2)\n",
+ "Requirement already satisfied: palettable>=3.3.3 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (3.3.3)\n",
+ "Requirement already satisfied: pandas>=2 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (2.2.2)\n",
+ "Requirement already satisfied: plotly>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (5.24.1)\n",
+ "Requirement already satisfied: pybtex>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (0.24.0)\n",
+ "Requirement already satisfied: requests>=2.32 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (2.32.3)\n",
+ "Requirement already satisfied: ruamel.yaml>=0.17.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (0.18.6)\n",
+ "Requirement already satisfied: scipy>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (1.13.1)\n",
+ "Requirement already satisfied: spglib>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (2.5.0)\n",
+ "Requirement already satisfied: sympy>=1.2 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (1.13.1)\n",
+ "Requirement already satisfied: tabulate>=0.9 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (0.9.0)\n",
+ "Requirement already satisfied: tqdm>=4.60 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (4.66.6)\n",
+ "Requirement already satisfied: uncertainties>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (3.2.2)\n",
+ "Requirement already satisfied: numpy<3,>=1.25.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (1.26.4)\n",
+ "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (1.3.0)\n",
+ "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (0.12.1)\n",
+ "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (4.54.1)\n",
+ "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (1.4.7)\n",
+ "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (24.1)\n",
+ "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (10.4.0)\n",
+ "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (3.2.0)\n",
+ "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.8->pymatgen) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2->pymatgen) (2024.2)\n",
+ "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=2->pymatgen) (2024.2)\n",
+ "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly>=4.5.0->pymatgen) (9.0.0)\n",
+ "Requirement already satisfied: PyYAML>=3.01 in /usr/local/lib/python3.10/dist-packages (from pybtex>=0.24.0->pymatgen) (6.0.2)\n",
+ "Requirement already satisfied: latexcodec>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from pybtex>=0.24.0->pymatgen) (3.0.0)\n",
+ "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from pybtex>=0.24.0->pymatgen) (1.16.0)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32->pymatgen) (3.4.0)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32->pymatgen) (3.10)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32->pymatgen) (2.2.3)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32->pymatgen) (2024.8.30)\n",
+ "Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from ruamel.yaml>=0.17.0->pymatgen) (0.2.12)\n",
+ "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.2->pymatgen) (1.3.0)\n"
+ ]
}
+ ],
+ "source": [
+ "!pip3 install pymatgen"
+ ]
},
- "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "fhA7AxnzeQfj",
+ "outputId": "3e9e530c-b84f-4285-8c00-659d64cb79b2"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "jd6K5XZdeAMu",
- "outputId": "974bad76-8527-47c4-f3b5-1fcd84c9c49b"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Collecting jarvis-tools\n",
- " Downloading jarvis_tools-2024.10.10-py2.py3-none-any.whl.metadata (3.1 kB)\n",
- "Requirement already satisfied: numpy>=1.20.1 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (1.26.4)\n",
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- "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (2.32.3)\n",
- "Requirement already satisfied: toolz>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (0.12.1)\n",
- "Collecting xmltodict>=0.11.0 (from jarvis-tools)\n",
- " Downloading xmltodict-0.14.2-py2.py3-none-any.whl.metadata (8.0 kB)\n",
- "Requirement already satisfied: tqdm>=4.41.1 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (4.66.6)\n",
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- "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (1.3.0)\n",
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- "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (3.2.0)\n",
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- "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools) (3.10)\n",
- "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools) (2.2.3)\n",
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- "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->jarvis-tools) (3.5.0)\n",
- "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.0.0->jarvis-tools) (1.16.0)\n",
- "Downloading jarvis_tools-2024.10.10-py2.py3-none-any.whl (4.2 MB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m22.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hDownloading xmltodict-0.14.2-py2.py3-none-any.whl (10.0 kB)\n",
- "Installing collected packages: xmltodict, jarvis-tools\n",
- "Successfully installed jarvis-tools-2024.10.10 xmltodict-0.14.2\n"
- ]
- }
- ],
- "source": [
- "!pip install jarvis-tools"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Looking in links: https://data.dgl.ai/wheels/torch-2.1/repo.html\n",
+ "Requirement already satisfied: dgl in /usr/local/lib/python3.10/dist-packages (2.4.0)\n",
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+ "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (1.26.4)\n",
+ "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from dgl) (24.1)\n",
+ "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from dgl) (2.2.2)\n",
+ "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (5.9.5)\n",
+ "Requirement already satisfied: pydantic>=2.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (2.9.2)\n",
+ "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from dgl) (6.0.2)\n",
+ "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (2.32.3)\n",
+ "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (1.13.1)\n",
+ "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from dgl) (4.66.6)\n",
+ "Requirement already satisfied: torch<=2.4.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (2.4.0)\n",
+ "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->dgl) (0.7.0)\n",
+ "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->dgl) (2.23.4)\n",
+ "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->dgl) (4.12.2)\n",
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+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (3.10)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (2.2.3)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (2024.8.30)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (3.16.1)\n",
+ "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (1.13.1)\n",
+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (3.1.4)\n",
+ "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (2024.10.0)\n",
+ "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (9.1.0.70)\n",
+ "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (12.1.3.1)\n",
+ "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (11.0.2.54)\n",
+ "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (10.3.2.106)\n",
+ "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (11.4.5.107)\n",
+ "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (12.1.0.106)\n",
+ "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (2.20.5)\n",
+ "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (12.1.105)\n",
+ "Requirement already satisfied: triton==3.0.0 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (3.0.0)\n",
+ "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch<=2.4.0->dgl) (12.6.77)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl) (2024.2)\n",
+ "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl) (2024.2)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->dgl) (1.16.0)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch<=2.4.0->dgl) (3.0.2)\n",
+ "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch<=2.4.0->dgl) (1.3.0)\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install dgl -f https://data.dgl.ai/wheels/torch-2.1/repo.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "YM0zUT9-fqfc",
+ "outputId": "6bca17f0-06c7-4559-9b81-a79c3739a116"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "!pip3 install pymatgen"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "mC4gkbnBeP7t",
- "outputId": "e8b7d83a-2c04-4909-e84d-1bdf75b8f42d"
- },
- "execution_count": 7,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Requirement already satisfied: pymatgen in /usr/local/lib/python3.10/dist-packages (2024.10.29)\n",
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- "Requirement already satisfied: pandas>=2 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (2.2.2)\n",
- "Requirement already satisfied: plotly>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (5.24.1)\n",
- "Requirement already satisfied: pybtex>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (0.24.0)\n",
- "Requirement already satisfied: requests>=2.32 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (2.32.3)\n",
- "Requirement already satisfied: ruamel.yaml>=0.17.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (0.18.6)\n",
- "Requirement already satisfied: scipy>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (1.13.1)\n",
- "Requirement already satisfied: spglib>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen) (2.5.0)\n",
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- ]
- }
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting matgl\n",
+ " Downloading matgl-1.1.3-py3-none-any.whl.metadata (16 kB)\n",
+ "Collecting ase (from matgl)\n",
+ " Downloading ase-3.23.0-py3-none-any.whl.metadata (3.8 kB)\n",
+ "Requirement already satisfied: dgl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from matgl) (2.4.0)\n",
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+ "Collecting lightning (from matgl)\n",
+ " Downloading lightning-2.4.0-py3-none-any.whl.metadata (38 kB)\n",
+ "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from matgl) (2.4.0)\n",
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+ "Collecting lightning-utilities<2.0,>=0.10.0 (from lightning->matgl)\n",
+ " Downloading lightning_utilities-0.11.8-py3-none-any.whl.metadata (5.2 kB)\n",
+ "Collecting torchmetrics<3.0,>=0.7.0 (from lightning->matgl)\n",
+ " Downloading torchmetrics-1.5.1-py3-none-any.whl.metadata (20 kB)\n",
+ "Collecting pytorch-lightning (from lightning->matgl)\n",
+ " Downloading pytorch_lightning-2.4.0-py3-none-any.whl.metadata (21 kB)\n",
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+ "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (1.3.1)\n",
+ "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (24.2.0)\n",
+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (1.5.0)\n",
+ "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (6.1.0)\n",
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+ "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (4.0.3)\n",
+ "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from yarl<2.0,>=1.12.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (0.2.0)\n",
+ "Downloading matgl-1.1.3-py3-none-any.whl (223 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m223.3/223.3 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading torchdata-0.7.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.7 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m4.7/4.7 MB\u001b[0m \u001b[31m61.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading ase-3.23.0-py3-none-any.whl (2.9 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m72.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading lightning-2.4.0-py3-none-any.whl (810 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m811.0/811.0 kB\u001b[0m \u001b[31m34.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading lightning_utilities-0.11.8-py3-none-any.whl (26 kB)\n",
+ "Downloading torchmetrics-1.5.1-py3-none-any.whl (890 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m890.6/890.6 kB\u001b[0m \u001b[31m45.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading pytorch_lightning-2.4.0-py3-none-any.whl (815 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m815.2/815.2 kB\u001b[0m \u001b[31m42.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hInstalling collected packages: lightning-utilities, ase, torchmetrics, torchdata, pytorch-lightning, lightning, matgl\n",
+ "Successfully installed ase-3.23.0 lightning-2.4.0 lightning-utilities-0.11.8 matgl-1.1.3 pytorch-lightning-2.4.0 torchdata-0.7.1 torchmetrics-1.5.1\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip3 install matgl"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "id": "avglyJbheVCr"
+ },
+ "outputs": [],
+ "source": [
+ "from __future__ import annotations\n",
+ "\n",
+ "import os\n",
+ "import shutil\n",
+ "import warnings\n",
+ "import zipfile\n",
+ "import matgl\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import pytorch_lightning as pl\n",
+ "import torch\n",
+ "import pickle\n",
+ "import numpy as np\n",
+ "from dgl.data.utils import split_dataset\n",
+ "from pymatgen.core import Structure\n",
+ "from pytorch_lightning.loggers import CSVLogger\n",
+ "from lightning.pytorch import Trainer\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "from matgl.ext.pymatgen import Structure2Graph, get_element_list\n",
+ "from matgl.graph.data import MGLDataset, MGLDataLoader #collate_fn. - shivani i don't think you need this as num_workers=0\n",
+ "from matgl.layers import BondExpansion\n",
+ "from matgl.models import MEGNet\n",
+ "from matgl.utils.io import RemoteFile\n",
+ "from matgl.utils.training import ModelLightningModule\n",
+ "\n",
+ "# To suppress warnings for clearer output\n",
+ "warnings.simplefilter(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "vZFdOECqfefs",
+ "outputId": "e722910c-d5f9-48c0-85af-fb7536d81ce7"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "!pip install dgl -f https://data.dgl.ai/wheels/torch-2.1/repo.html"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "fhA7AxnzeQfj",
- "outputId": "3e9e530c-b84f-4285-8c00-659d64cb79b2"
- },
- "execution_count": 6,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Looking in links: https://data.dgl.ai/wheels/torch-2.1/repo.html\n",
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- "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (12.1.3.1)\n",
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- ]
- }
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Obtaining 3D dataset 76k ...\n",
+ "Reference:https://www.nature.com/articles/s41524-020-00440-1\n",
+ "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n"
+ ]
},
{
- "cell_type": "code",
- "source": [
- "!pip3 install matgl"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "YM0zUT9-fqfc",
- "outputId": "6bca17f0-06c7-4559-9b81-a79c3739a116"
- },
- "execution_count": 9,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
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- "\u001b[?25hDownloading lightning-2.4.0-py3-none-any.whl (810 kB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m811.0/811.0 kB\u001b[0m \u001b[31m34.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hDownloading lightning_utilities-0.11.8-py3-none-any.whl (26 kB)\n",
- "Downloading torchmetrics-1.5.1-py3-none-any.whl (890 kB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m890.6/890.6 kB\u001b[0m \u001b[31m45.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hDownloading pytorch_lightning-2.4.0-py3-none-any.whl (815 kB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m815.2/815.2 kB\u001b[0m \u001b[31m42.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hInstalling collected packages: lightning-utilities, ase, torchmetrics, torchdata, pytorch-lightning, lightning, matgl\n",
- "Successfully installed ase-3.23.0 lightning-2.4.0 lightning-utilities-0.11.8 matgl-1.1.3 pytorch-lightning-2.4.0 torchdata-0.7.1 torchmetrics-1.5.1\n"
- ]
- }
- ]
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 40.8M/40.8M [00:01<00:00, 20.5MiB/s]\n"
+ ]
},
{
- "cell_type": "code",
- "source": [
- "from __future__ import annotations\n",
- "\n",
- "import os\n",
- "import shutil\n",
- "import warnings\n",
- "import zipfile\n",
- "import matgl\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import pandas as pd\n",
- "import pytorch_lightning as pl\n",
- "import torch\n",
- "import pickle\n",
- "import numpy as np\n",
- "from dgl.data.utils import split_dataset\n",
- "from pymatgen.core import Structure\n",
- "from pytorch_lightning.loggers import CSVLogger\n",
- "from lightning.pytorch import Trainer\n",
- "from tqdm import tqdm\n",
- "\n",
- "from matgl.ext.pymatgen import Structure2Graph, get_element_list\n",
- "from matgl.graph.data import MGLDataset, MGLDataLoader #collate_fn. - shivani i don't think you need this as num_workers=0\n",
- "from matgl.layers import BondExpansion\n",
- "from matgl.models import MEGNet\n",
- "from matgl.utils.io import RemoteFile\n",
- "from matgl.utils.training import ModelLightningModule\n",
- "\n",
- "# To suppress warnings for clearer output\n",
- "warnings.simplefilter(\"ignore\")"
- ],
- "metadata": {
- "id": "avglyJbheVCr"
- },
- "execution_count": 39,
- "outputs": []
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading the zipfile...\n",
+ "Loading completed.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from jarvis.db.figshare import data\n",
+ "\n",
+ "dft_3d = data('dft_3d')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "o1-OpcxGgXn3",
+ "outputId": "eb48c546-8539-4acc-f8cd-290039cd1a6f"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "from jarvis.db.figshare import data\n",
- "\n",
- "dft_3d = data('dft_3d')"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "vZFdOECqfefs",
- "outputId": "e722910c-d5f9-48c0-85af-fb7536d81ce7"
- },
- "execution_count": 12,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Obtaining 3D dataset 76k ...\n",
- "Reference:https://www.nature.com/articles/s41524-020-00440-1\n",
- "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "100%|██████████| 40.8M/40.8M [00:01<00:00, 20.5MiB/s]\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Loading the zipfile...\n",
- "Loading completed.\n"
- ]
- }
+ "data": {
+ "text/plain": [
+ "dict_keys(['jid', 'spg_number', 'spg_symbol', 'formula', 'formation_energy_peratom', 'func', 'optb88vdw_bandgap', 'atoms', 'slme', 'magmom_oszicar', 'spillage', 'elastic_tensor', 'effective_masses_300K', 'kpoint_length_unit', 'maxdiff_mesh', 'maxdiff_bz', 'encut', 'optb88vdw_total_energy', 'epsx', 'epsy', 'epsz', 'mepsx', 'mepsy', 'mepsz', 'modes', 'magmom_outcar', 'max_efg', 'avg_elec_mass', 'avg_hole_mass', 'icsd', 'dfpt_piezo_max_eij', 'dfpt_piezo_max_dij', 'dfpt_piezo_max_dielectric', 'dfpt_piezo_max_dielectric_electronic', 'dfpt_piezo_max_dielectric_ionic', 'max_ir_mode', 'min_ir_mode', 'n-Seebeck', 'p-Seebeck', 'n-powerfact', 'p-powerfact', 'ncond', 'pcond', 'nkappa', 'pkappa', 'ehull', 'Tc_supercon', 'dimensionality', 'efg', 'xml_data_link', 'typ', 'exfoliation_energy', 'spg', 'crys', 'density', 'poisson', 'raw_files', 'nat', 'bulk_modulus_kv', 'shear_modulus_gv', 'mbj_bandgap', 'hse_gap', 'reference', 'search'])"
]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dft_3d[0].keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "id": "RnAkEQBSgZki"
+ },
+ "outputs": [],
+ "source": [
+ "## Let's make a dataframe from this:\n",
+ "import pandas as pd\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 429
},
+ "id": "HUvXBAGDgdYn",
+ "outputId": "cb02920a-b7e5-469e-8b8e-8fb55a7177e3"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "dft_3d[0].keys()"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "o1-OpcxGgXn3",
- "outputId": "eb48c546-8539-4acc-f8cd-290039cd1a6f"
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "dataframe",
+ "variable_name": "df"
},
- "execution_count": 13,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "dict_keys(['jid', 'spg_number', 'spg_symbol', 'formula', 'formation_energy_peratom', 'func', 'optb88vdw_bandgap', 'atoms', 'slme', 'magmom_oszicar', 'spillage', 'elastic_tensor', 'effective_masses_300K', 'kpoint_length_unit', 'maxdiff_mesh', 'maxdiff_bz', 'encut', 'optb88vdw_total_energy', 'epsx', 'epsy', 'epsz', 'mepsx', 'mepsy', 'mepsz', 'modes', 'magmom_outcar', 'max_efg', 'avg_elec_mass', 'avg_hole_mass', 'icsd', 'dfpt_piezo_max_eij', 'dfpt_piezo_max_dij', 'dfpt_piezo_max_dielectric', 'dfpt_piezo_max_dielectric_electronic', 'dfpt_piezo_max_dielectric_ionic', 'max_ir_mode', 'min_ir_mode', 'n-Seebeck', 'p-Seebeck', 'n-powerfact', 'p-powerfact', 'ncond', 'pcond', 'nkappa', 'pkappa', 'ehull', 'Tc_supercon', 'dimensionality', 'efg', 'xml_data_link', 'typ', 'exfoliation_energy', 'spg', 'crys', 'density', 'poisson', 'raw_files', 'nat', 'bulk_modulus_kv', 'shear_modulus_gv', 'mbj_bandgap', 'hse_gap', 'reference', 'search'])"
- ]
- },
- "metadata": {},
- "execution_count": 13
- }
+ "text/html": [
+ "\n",
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+ " <div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>jid</th>\n",
+ " <th>spg_number</th>\n",
+ " <th>spg_symbol</th>\n",
+ " <th>formula</th>\n",
+ " <th>formation_energy_peratom</th>\n",
+ " <th>func</th>\n",
+ " <th>optb88vdw_bandgap</th>\n",
+ " <th>atoms</th>\n",
+ " <th>slme</th>\n",
+ " <th>magmom_oszicar</th>\n",
+ " <th>...</th>\n",
+ " <th>density</th>\n",
+ " <th>poisson</th>\n",
+ " <th>raw_files</th>\n",
+ " <th>nat</th>\n",
+ " <th>bulk_modulus_kv</th>\n",
+ " <th>shear_modulus_gv</th>\n",
+ " <th>mbj_bandgap</th>\n",
+ " <th>hse_gap</th>\n",
+ " <th>reference</th>\n",
+ " <th>search</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>JVASP-90856</td>\n",
+ " <td>129</td>\n",
+ " <td>P4/nmm</td>\n",
+ " <td>TiCuSiAs</td>\n",
+ " <td>-0.42762</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[3.566933224304235, 0.0, -0.0...</td>\n",
+ " <td>na</td>\n",
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+ " <td>5.956</td>\n",
+ " <td>na</td>\n",
+ " <td>[]</td>\n",
+ " <td>8</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-1080455</td>\n",
+ " <td>-As-Cu-Si-Ti</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>JVASP-86097</td>\n",
+ " <td>221</td>\n",
+ " <td>Pm-3m</td>\n",
+ " <td>DyB6</td>\n",
+ " <td>-0.41596</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[4.089078911208881, 0.0, 0.0]...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.522</td>\n",
+ " <td>na</td>\n",
+ " <td>[OPT-LOPTICS,JVASP-86097.zip,https://ndownload...</td>\n",
+ " <td>7</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-568319</td>\n",
+ " <td>-B-Dy</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>JVASP-64906</td>\n",
+ " <td>119</td>\n",
+ " <td>I-4m2</td>\n",
+ " <td>Be2OsRu</td>\n",
+ " <td>0.04847</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[-1.833590720595598, 1.833590...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>10.960</td>\n",
+ " <td>na</td>\n",
+ " <td>[OPT-LOPTICS,JVASP-64906.zip,https://ndownload...</td>\n",
+ " <td>4</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>auid-3eaf68dd483bf4f4</td>\n",
+ " <td>-Be-Os-Ru</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>JVASP-98225</td>\n",
+ " <td>14</td>\n",
+ " <td>P2_1/c</td>\n",
+ " <td>KBi</td>\n",
+ " <td>-0.44140</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.472</td>\n",
+ " <td>{'lattice_mat': [[7.2963518353359165, 0.0, 0.0...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.145</td>\n",
+ " <td>na</td>\n",
+ " <td>[]</td>\n",
+ " <td>32</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-31104</td>\n",
+ " <td>-Bi-K</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>JVASP-10</td>\n",
+ " <td>164</td>\n",
+ " <td>P-3m1</td>\n",
+ " <td>VSe2</td>\n",
+ " <td>-0.71026</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[1.6777483798834445, -2.90594...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.718</td>\n",
+ " <td>0.23</td>\n",
+ " <td>[FD-ELAST,JVASP-10.zip,https://ndownloader.fig...</td>\n",
+ " <td>3</td>\n",
+ " <td>48.79</td>\n",
+ " <td>33.05</td>\n",
+ " <td>0.0</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-694</td>\n",
+ " <td>-Se-V</td>\n",
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+ " .colab-df-convert:hover {\n",
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+ " const buttonEl =\n",
+ " document.querySelector('#df-7bedbd8a-b0f7-4b76-aac4-3afa628ac70d button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-7bedbd8a-b0f7-4b76-aac4-3afa628ac70d');\n",
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+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
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+ " height: 32px;\n",
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+ "\n",
+ " .colab-df-quickchart:hover {\n",
+ " background-color: var(--hover-bg-color);\n",
+ " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: var(--button-hover-fill-color);\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart-complete:disabled,\n",
+ " .colab-df-quickchart-complete:disabled:hover {\n",
+ " background-color: var(--disabled-bg-color);\n",
+ " fill: var(--disabled-fill-color);\n",
+ " box-shadow: none;\n",
+ " }\n",
+ "\n",
+ " .colab-df-spinner {\n",
+ " border: 2px solid var(--fill-color);\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " animation:\n",
+ " spin 1s steps(1) infinite;\n",
+ " }\n",
+ "\n",
+ " @keyframes spin {\n",
+ " 0% {\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " border-left-color: var(--fill-color);\n",
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+ " border-left-color: var(--fill-color);\n",
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+ " }\n",
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+ " border-right-color: var(--fill-color);\n",
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+ " }\n",
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+ " border-color: transparent;\n",
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+ " }\n",
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+ " border-right-color: var(--fill-color);\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " }\n",
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+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " }\n",
+ " }\n",
+ "</style>\n",
+ "\n",
+ " <script>\n",
+ " async function quickchart(key) {\n",
+ " const quickchartButtonEl =\n",
+ " document.querySelector('#' + key + ' button');\n",
+ " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
+ " quickchartButtonEl.classList.add('colab-df-spinner');\n",
+ " try {\n",
+ " const charts = await google.colab.kernel.invokeFunction(\n",
+ " 'suggestCharts', [key], {});\n",
+ " } catch (error) {\n",
+ " console.error('Error during call to suggestCharts:', error);\n",
+ " }\n",
+ " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+ " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+ " }\n",
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+ " let quickchartButtonEl =\n",
+ " document.querySelector('#df-bd8b8a00-937e-4b1d-a50c-05a9e032c404 button');\n",
+ " quickchartButtonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ " })();\n",
+ " </script>\n",
+ "</div>\n",
+ "\n",
+ " </div>\n",
+ " </div>\n"
+ ],
+ "text/plain": [
+ " jid spg_number spg_symbol formula formation_energy_peratom \\\n",
+ "0 JVASP-90856 129 P4/nmm TiCuSiAs -0.42762 \n",
+ "1 JVASP-86097 221 Pm-3m DyB6 -0.41596 \n",
+ "2 JVASP-64906 119 I-4m2 Be2OsRu 0.04847 \n",
+ "3 JVASP-98225 14 P2_1/c KBi -0.44140 \n",
+ "4 JVASP-10 164 P-3m1 VSe2 -0.71026 \n",
+ "\n",
+ " func optb88vdw_bandgap \\\n",
+ "0 OptB88vdW 0.000 \n",
+ "1 OptB88vdW 0.000 \n",
+ "2 OptB88vdW 0.000 \n",
+ "3 OptB88vdW 0.472 \n",
+ "4 OptB88vdW 0.000 \n",
+ "\n",
+ " atoms slme magmom_oszicar ... \\\n",
+ "0 {'lattice_mat': [[3.566933224304235, 0.0, -0.0... na 0.0 ... \n",
+ "1 {'lattice_mat': [[4.089078911208881, 0.0, 0.0]... na 0.0 ... \n",
+ "2 {'lattice_mat': [[-1.833590720595598, 1.833590... na 0.0 ... \n",
+ "3 {'lattice_mat': [[7.2963518353359165, 0.0, 0.0... na 0.0 ... \n",
+ "4 {'lattice_mat': [[1.6777483798834445, -2.90594... na 0.0 ... \n",
+ "\n",
+ " density poisson raw_files nat \\\n",
+ "0 5.956 na [] 8 \n",
+ "1 5.522 na [OPT-LOPTICS,JVASP-86097.zip,https://ndownload... 7 \n",
+ "2 10.960 na [OPT-LOPTICS,JVASP-64906.zip,https://ndownload... 4 \n",
+ "3 5.145 na [] 32 \n",
+ "4 5.718 0.23 [FD-ELAST,JVASP-10.zip,https://ndownloader.fig... 3 \n",
+ "\n",
+ " bulk_modulus_kv shear_modulus_gv mbj_bandgap hse_gap \\\n",
+ "0 na na na na \n",
+ "1 na na na na \n",
+ "2 na na na na \n",
+ "3 na na na na \n",
+ "4 48.79 33.05 0.0 na \n",
+ "\n",
+ " reference search \n",
+ "0 mp-1080455 -As-Cu-Si-Ti \n",
+ "1 mp-568319 -B-Dy \n",
+ "2 auid-3eaf68dd483bf4f4 -Be-Os-Ru \n",
+ "3 mp-31104 -Bi-K \n",
+ "4 mp-694 -Se-V \n",
+ "\n",
+ "[5 rows x 64 columns]"
]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df=pd.DataFrame(dft_3d)\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "hrRk8GKighlh",
+ "outputId": "d54272bd-a432-462c-d89a-65241d14db65"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "## Let's make a dataframe from this:\n",
- "import pandas as pd\n",
- "import numpy as np"
- ],
- "metadata": {
- "id": "RnAkEQBSgZki"
- },
- "execution_count": 14,
- "outputs": []
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "jid 75993\n",
+ "spg_number 75993\n",
+ "spg_symbol 75993\n",
+ "formula 75993\n",
+ "formation_energy_peratom 75993\n",
+ "func 75993\n",
+ "optb88vdw_bandgap 75993\n",
+ "atoms 75993\n",
+ "slme 9770\n",
+ "magmom_oszicar 71320\n",
+ "spillage 11377\n",
+ "elastic_tensor 25513\n",
+ "effective_masses_300K 75993\n",
+ "kpoint_length_unit 75671\n",
+ "maxdiff_mesh 5861\n",
+ "maxdiff_bz 5861\n",
+ "encut 75670\n",
+ "optb88vdw_total_energy 75993\n",
+ "epsx 52168\n",
+ "epsy 52168\n",
+ "epsz 52168\n",
+ "mepsx 18293\n",
+ "mepsy 18293\n",
+ "mepsz 18293\n",
+ "modes 13910\n",
+ "magmom_outcar 74261\n",
+ "max_efg 11871\n",
+ "avg_elec_mass 17645\n",
+ "avg_hole_mass 17645\n",
+ "icsd 75993\n",
+ "dfpt_piezo_max_eij 4799\n",
+ "dfpt_piezo_max_dij 3347\n",
+ "dfpt_piezo_max_dielectric 4706\n",
+ "dfpt_piezo_max_dielectric_electronic 4809\n",
+ "dfpt_piezo_max_dielectric_ionic 4809\n",
+ "max_ir_mode 4805\n",
+ "min_ir_mode 4809\n",
+ "n-Seebeck 23218\n",
+ "p-Seebeck 23218\n",
+ "n-powerfact 23218\n",
+ "p-powerfact 23218\n",
+ "ncond 23218\n",
+ "pcond 23218\n",
+ "nkappa 23218\n",
+ "pkappa 23218\n",
+ "ehull 75993\n",
+ "Tc_supercon 1058\n",
+ "dimensionality 75560\n",
+ "efg 75993\n",
+ "xml_data_link 75993\n",
+ "typ 75993\n",
+ "exfoliation_energy 813\n",
+ "spg 75993\n",
+ "crys 75993\n",
+ "density 75993\n",
+ "poisson 23597\n",
+ "raw_files 75993\n",
+ "nat 75993\n",
+ "bulk_modulus_kv 23824\n",
+ "shear_modulus_gv 23824\n",
+ "mbj_bandgap 19805\n",
+ "hse_gap 56\n",
+ "reference 75993\n",
+ "search 75993\n"
+ ]
+ }
+ ],
+ "source": [
+ "## Count number of entries for each property\n",
+ "for i in df.columns.values:\n",
+ " val=df[i].replace('na',np.nan).dropna().values\n",
+ " print(i,len(val))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "id": "6dxg4ITfgkOE"
+ },
+ "outputs": [],
+ "source": [
+ "## Filter dataset based on desired property\n",
+ "## We will focus on elastic properties for today, i.e. Bulk modulus"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "id": "xcuLFYdNgq-u"
+ },
+ "outputs": [],
+ "source": [
+ "from jarvis.core.atoms import Atoms\n",
+ "bm=df[df.bulk_modulus_kv != 'na']\n",
+ "data = [(Atoms.from_dict(bm.iloc[i]['atoms']).pymatgen_converter(), bm.iloc[i].bulk_modulus_kv) for i in range(len(bm))]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "id": "Sc1zXAn4gtTT"
+ },
+ "outputs": [],
+ "source": [
+ "import itertools\n",
+ "def get_stoichiometry(elements):\n",
+ " return [(g[0], len(list(g[1]))) for g in itertools.groupby(elements)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "arU4jF5tgvt0",
+ "outputId": "c75f7f94-afe6-4d42-e81a-93405ddcc301"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "df=pd.DataFrame(dft_3d)\n",
- "df.head()"
- ],
- "metadata": {
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- "base_uri": "https://localhost:8080/",
- "height": 429
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- "id": "HUvXBAGDgdYn",
- "outputId": "cb02920a-b7e5-469e-8b8e-8fb55a7177e3"
- },
- "execution_count": 15,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- " jid spg_number spg_symbol formula formation_energy_peratom \\\n",
- "0 JVASP-90856 129 P4/nmm TiCuSiAs -0.42762 \n",
- "1 JVASP-86097 221 Pm-3m DyB6 -0.41596 \n",
- "2 JVASP-64906 119 I-4m2 Be2OsRu 0.04847 \n",
- "3 JVASP-98225 14 P2_1/c KBi -0.44140 \n",
- "4 JVASP-10 164 P-3m1 VSe2 -0.71026 \n",
- "\n",
- " func optb88vdw_bandgap \\\n",
- "0 OptB88vdW 0.000 \n",
- "1 OptB88vdW 0.000 \n",
- "2 OptB88vdW 0.000 \n",
- "3 OptB88vdW 0.472 \n",
- "4 OptB88vdW 0.000 \n",
- "\n",
- " atoms slme magmom_oszicar ... \\\n",
- "0 {'lattice_mat': [[3.566933224304235, 0.0, -0.0... na 0.0 ... \n",
- "1 {'lattice_mat': [[4.089078911208881, 0.0, 0.0]... na 0.0 ... \n",
- "2 {'lattice_mat': [[-1.833590720595598, 1.833590... na 0.0 ... \n",
- "3 {'lattice_mat': [[7.2963518353359165, 0.0, 0.0... na 0.0 ... \n",
- "4 {'lattice_mat': [[1.6777483798834445, -2.90594... na 0.0 ... \n",
- "\n",
- " density poisson raw_files nat \\\n",
- "0 5.956 na [] 8 \n",
- "1 5.522 na [OPT-LOPTICS,JVASP-86097.zip,https://ndownload... 7 \n",
- "2 10.960 na [OPT-LOPTICS,JVASP-64906.zip,https://ndownload... 4 \n",
- "3 5.145 na [] 32 \n",
- "4 5.718 0.23 [FD-ELAST,JVASP-10.zip,https://ndownloader.fig... 3 \n",
- "\n",
- " bulk_modulus_kv shear_modulus_gv mbj_bandgap hse_gap \\\n",
- "0 na na na na \n",
- "1 na na na na \n",
- "2 na na na na \n",
- "3 na na na na \n",
- "4 48.79 33.05 0.0 na \n",
- "\n",
- " reference search \n",
- "0 mp-1080455 -As-Cu-Si-Ti \n",
- "1 mp-568319 -B-Dy \n",
- "2 auid-3eaf68dd483bf4f4 -Be-Os-Ru \n",
- "3 mp-31104 -Bi-K \n",
- "4 mp-694 -Se-V \n",
- "\n",
- "[5 rows x 64 columns]"
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- " <th>0</th>\n",
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- " <th>1</th>\n",
- " <td>JVASP-86097</td>\n",
- " <td>221</td>\n",
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- " <td>DyB6</td>\n",
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- " <td>119</td>\n",
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- " <td>0.04847</td>\n",
- " <td>OptB88vdW</td>\n",
- " <td>0.000</td>\n",
- " <td>{'lattice_mat': [[-1.833590720595598, 1.833590...</td>\n",
- " <td>na</td>\n",
- " <td>0.0</td>\n",
- " <td>...</td>\n",
- " <td>10.960</td>\n",
- " <td>na</td>\n",
- " <td>[OPT-LOPTICS,JVASP-64906.zip,https://ndownload...</td>\n",
- " <td>4</td>\n",
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- " <td>14</td>\n",
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- " <td>P-3m1</td>\n",
- " <td>VSe2</td>\n",
- " <td>-0.71026</td>\n",
- " <td>OptB88vdW</td>\n",
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- "\n",
- " const docLinkHtml = 'Like what you see? Visit the ' +\n",
- " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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- " const quickchartButtonEl =\n",
- " document.querySelector('#' + key + ' button');\n",
- " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
- " quickchartButtonEl.classList.add('colab-df-spinner');\n",
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- " const charts = await google.colab.kernel.invokeFunction(\n",
- " 'suggestCharts', [key], {});\n",
- " } catch (error) {\n",
- " console.error('Error during call to suggestCharts:', error);\n",
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- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "dataframe",
- "variable_name": "df"
- }
- },
- "metadata": {},
- "execution_count": 15
- }
- ]
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 23824/23824 [00:25<00:00, 921.20it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "## Use all the material dataset for training the bulk modulus\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "stoichs=[] #stoichiometry\n",
+ "bulk=[] #bulk modulus\n",
+ "for i in tqdm(range(len(bm))):\n",
+ " stoichs.append(Atoms.from_dict(bm.iloc[i]['atoms']).pymatgen_converter())\n",
+ " bulk.append(bm.iloc[i]['bulk_modulus_kv'])\n",
+ "data_ran=list(zip(stoichs,bulk))\n",
+ "#write out the dataset, to train later\n",
+ "import pickle\n",
+ "with open('data_ran.pickle', 'wb') as f:\n",
+ " pickle.dump(data_ran, f)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "id": "CFgTo75EgzKR"
+ },
+ "outputs": [],
+ "source": [
+ "#read in the dataset\n",
+ "data_ran=pd.read_pickle('./data_ran.pickle')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "BWZuJzqNg-ak",
+ "outputId": "8076df63-2b15-4739-f5fe-9f703b68db6f"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "## Count number of entries for each property\n",
- "for i in df.columns.values:\n",
- " val=df[i].replace('na',np.nan).dropna().values\n",
- " print(i,len(val))"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "hrRk8GKighlh",
- "outputId": "d54272bd-a432-462c-d89a-65241d14db65"
- },
- "execution_count": 16,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "jid 75993\n",
- "spg_number 75993\n",
- "spg_symbol 75993\n",
- "formula 75993\n",
- "formation_energy_peratom 75993\n",
- "func 75993\n",
- "optb88vdw_bandgap 75993\n",
- "atoms 75993\n",
- "slme 9770\n",
- "magmom_oszicar 71320\n",
- "spillage 11377\n",
- "elastic_tensor 25513\n",
- "effective_masses_300K 75993\n",
- "kpoint_length_unit 75671\n",
- "maxdiff_mesh 5861\n",
- "maxdiff_bz 5861\n",
- "encut 75670\n",
- "optb88vdw_total_energy 75993\n",
- "epsx 52168\n",
- "epsy 52168\n",
- "epsz 52168\n",
- "mepsx 18293\n",
- "mepsy 18293\n",
- "mepsz 18293\n",
- "modes 13910\n",
- "magmom_outcar 74261\n",
- "max_efg 11871\n",
- "avg_elec_mass 17645\n",
- "avg_hole_mass 17645\n",
- "icsd 75993\n",
- "dfpt_piezo_max_eij 4799\n",
- "dfpt_piezo_max_dij 3347\n",
- "dfpt_piezo_max_dielectric 4706\n",
- "dfpt_piezo_max_dielectric_electronic 4809\n",
- "dfpt_piezo_max_dielectric_ionic 4809\n",
- "max_ir_mode 4805\n",
- "min_ir_mode 4809\n",
- "n-Seebeck 23218\n",
- "p-Seebeck 23218\n",
- "n-powerfact 23218\n",
- "p-powerfact 23218\n",
- "ncond 23218\n",
- "pcond 23218\n",
- "nkappa 23218\n",
- "pkappa 23218\n",
- "ehull 75993\n",
- "Tc_supercon 1058\n",
- "dimensionality 75560\n",
- "efg 75993\n",
- "xml_data_link 75993\n",
- "typ 75993\n",
- "exfoliation_energy 813\n",
- "spg 75993\n",
- "crys 75993\n",
- "density 75993\n",
- "poisson 23597\n",
- "raw_files 75993\n",
- "nat 75993\n",
- "bulk_modulus_kv 23824\n",
- "shear_modulus_gv 23824\n",
- "mbj_bandgap 19805\n",
- "hse_gap 56\n",
- "reference 75993\n",
- "search 75993\n"
- ]
- }
+ "data": {
+ "text/plain": [
+ "list"
]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(data_ran)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "TG4g1Dp4hBhg",
+ "outputId": "a3adbd5b-95c9-4ec9-8cd0-4c39136ea699"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "## Filter dataset based on desired property\n",
- "## We will focus on elastic properties for today, i.e. Bulk modulus"
- ],
- "metadata": {
- "id": "6dxg4ITfgkOE"
- },
- "execution_count": 17,
- "outputs": []
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Full Formula (Li4 Ce4 O8)\n",
+ "Reduced Formula: LiCeO2\n",
+ "abc : 5.778710 5.859847 6.029586\n",
+ "angles: 90.000000 90.000000 103.986129\n",
+ "pbc : True True True\n",
+ "Sites (16)\n",
+ " # SP a b c\n",
+ "--- ---- -------- -------- --------\n",
+ " 0 Li 0.182502 0.662954 0.132604\n",
+ " 1 Li 0.317498 0.337046 0.632604\n",
+ " 2 Li 0.817498 0.337046 0.867396\n",
+ " 3 Li 0.682502 0.662954 0.367396\n",
+ " 4 Ce 0.303409 0.200495 0.071539\n",
+ " 5 Ce 0.803409 0.200495 0.428461\n",
+ " 6 Ce 0.696591 0.799505 0.928461\n",
+ " 7 Ce 0.196591 0.799505 0.571539\n",
+ " 8 O 0.986063 0.906145 0.246099\n",
+ " 9 O 0.696592 0.43465 0.136137\n",
+ " 10 O 0.513937 0.093855 0.746099\n",
+ " 11 O 0.013937 0.093855 0.753901\n",
+ " 12 O 0.303408 0.56535 0.863863\n",
+ " 13 O 0.803408 0.56535 0.636137\n",
+ " 14 O 0.196592 0.43465 0.363863\n",
+ " 15 O 0.486063 0.906145 0.253901 2.0546896429499797\n"
+ ]
+ }
+ ],
+ "source": [
+ "import random\n",
+ "import numpy as np\n",
+ "\n",
+ "\n",
+ "random.shuffle(data_ran)\n",
+ "\n",
+ "structures=[d[0] for d in data_ran[:15000]]\n",
+ "targets=np.log10([d[1] for d in data_ran])\n",
+ "\n",
+ "print(structures[0],targets[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "Z-jazl2PhHsP",
+ "outputId": "0044297b-6dc9-4f70-83f0-afb9c4c558d7"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "from jarvis.core.atoms import Atoms\n",
- "bm=df[df.bulk_modulus_kv != 'na']\n",
- "data = [(Atoms.from_dict(bm.iloc[i]['atoms']).pymatgen_converter(), bm.iloc[i].bulk_modulus_kv) for i in range(len(bm))]"
- ],
- "metadata": {
- "id": "xcuLFYdNgq-u"
- },
- "execution_count": 18,
- "outputs": []
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 15000/15000 [00:24<00:00, 616.21it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# get element types in the dataset\n",
+ "elem_list = get_element_list(structures)\n",
+ "# setup a graph converter\n",
+ "converter = Structure2Graph(element_types=elem_list, cutoff=4.0)\n",
+ "# convert the raw dataset into MEGNetDataset\n",
+ "mp_dataset = MGLDataset(\n",
+ " structures=structures,\n",
+ " labels={\"bulk_modulus_kv\": targets},\n",
+ " converter=converter,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "id": "QPyjAxGghK0Q"
+ },
+ "outputs": [],
+ "source": [
+ "train_data, val_data, test_data = split_dataset(\n",
+ " mp_dataset,\n",
+ " frac_list=[0.6, 0.2, 0.2],\n",
+ " shuffle=True,\n",
+ " random_state=42,\n",
+ ")\n",
+ "train_loader, val_loader, test_loader = MGLDataLoader(\n",
+ " train_data=train_data,\n",
+ " val_data=val_data,\n",
+ " test_data=test_data,\n",
+ " batch_size=64,\n",
+ " num_workers=0,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "id": "pYGXwyZphQtd"
+ },
+ "outputs": [],
+ "source": [
+ "# setup the embedding layer for node attributes\n",
+ "node_embed = torch.nn.Embedding(len(elem_list), 16)\n",
+ "# define the bond expansion\n",
+ "bond_expansion = BondExpansion(rbf_type=\"Gaussian\", initial=0.0, final=5.0, num_centers=100, width=0.5)\n",
+ "\n",
+ "# setup the architecture of MEGNet model\n",
+ "model = MEGNet(\n",
+ " dim_node_embedding=16,\n",
+ " dim_edge_embedding=100,\n",
+ " dim_state_embedding=2,\n",
+ " nblocks=3,\n",
+ " hidden_layer_sizes_input=(64, 32),\n",
+ " hidden_layer_sizes_conv=(64, 64, 32),\n",
+ " nlayers_set2set=1,\n",
+ " niters_set2set=2,\n",
+ " hidden_layer_sizes_output=(32, 16),\n",
+ " is_classification=False,\n",
+ " activation_type=\"softplus2\",\n",
+ " bond_expansion=bond_expansion,\n",
+ " #collate_fn=collate_fn, shivani - not needed now?\n",
+ " cutoff=4.0,\n",
+ " gauss_width=0.5,\n",
+ ")\n",
+ "\n",
+ "# setup the MEGNetTrainer\n",
+ "lit_module = ModelLightningModule(model=model)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "colab": {
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+ "348fc0c1069a4430be11d2112f212080",
+ "0c134e301fe5481bbcd47eb35ff1ecd9",
+ "cda06210315d42f3b4909bdc14310e15",
+ "98131182118a4e0bbb0de266875c10ec",
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+ "b70eedcca7744a8dab61e3c5796e2072",
+ "3ccc25decd5b48e39bd101a6a526865e",
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+ ]
},
+ "id": "nqWWnzQUhoki",
+ "outputId": "71988016-cb28-41b3-ea83-4400f135f481"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "import itertools\n",
- "def get_stoichiometry(elements):\n",
- " return [(g[0], len(list(g[1]))) for g in itertools.groupby(elements)]"
- ],
- "metadata": {
- "id": "Sc1zXAn4gtTT"
- },
- "execution_count": 19,
- "outputs": []
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO: GPU available: False, used: False\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n",
+ "INFO: TPU available: False, using: 0 TPU cores\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
+ "INFO: HPU available: False, using: 0 HPUs\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
+ "INFO: \n",
+ " | Name | Type | Params | Mode \n",
+ "----------------------------------------------------\n",
+ "0 | model | MEGNet | 189 K | train\n",
+ "1 | mae | MeanAbsoluteError | 0 | train\n",
+ "2 | rmse | MeanSquaredError | 0 | train\n",
+ "----------------------------------------------------\n",
+ "189 K Trainable params\n",
+ "100 Non-trainable params\n",
+ "189 K Total params\n",
+ "0.758 Total estimated model params size (MB)\n",
+ "109 Modules in train mode\n",
+ "0 Modules in eval mode\n",
+ "INFO:lightning.pytorch.callbacks.model_summary:\n",
+ " | Name | Type | Params | Mode \n",
+ "----------------------------------------------------\n",
+ "0 | model | MEGNet | 189 K | train\n",
+ "1 | mae | MeanAbsoluteError | 0 | train\n",
+ "2 | rmse | MeanSquaredError | 0 | train\n",
+ "----------------------------------------------------\n",
+ "189 K Trainable params\n",
+ "100 Non-trainable params\n",
+ "189 K Total params\n",
+ "0.758 Total estimated model params size (MB)\n",
+ "109 Modules in train mode\n",
+ "0 Modules in eval mode\n"
+ ]
},
{
- "cell_type": "code",
- "source": [
- "## Use all the material dataset for training the bulk modulus\n",
- "from tqdm import tqdm\n",
- "\n",
- "stoichs=[] #stoichiometry\n",
- "bulk=[] #bulk modulus\n",
- "for i in tqdm(range(len(bm))):\n",
- " stoichs.append(Atoms.from_dict(bm.iloc[i]['atoms']).pymatgen_converter())\n",
- " bulk.append(bm.iloc[i]['bulk_modulus_kv'])\n",
- "data_ran=list(zip(stoichs,bulk))\n",
- "#write out the dataset, to train later\n",
- "import pickle\n",
- "with open('data_ran.pickle', 'wb') as f:\n",
- " pickle.dump(data_ran, f)"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "arU4jF5tgvt0",
- "outputId": "c75f7f94-afe6-4d42-e81a-93405ddcc301"
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "768ce9da55994740bd19d449bd0880db",
+ "version_major": 2,
+ "version_minor": 0
},
- "execution_count": 20,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "100%|██████████| 23824/23824 [00:25<00:00, 921.20it/s]\n"
- ]
- }
+ "text/plain": [
+ "Sanity Checking: | | 0/? [00:00<?, ?it/s]"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "source": [
- "#read in the dataset\n",
- "data_ran=pd.read_pickle('./data_ran.pickle')"
- ],
- "metadata": {
- "id": "CFgTo75EgzKR"
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "d9d6aacd59ea4fcf9c0f4224b377c610",
+ "version_major": 2,
+ "version_minor": 0
},
- "execution_count": 22,
- "outputs": []
+ "text/plain": [
+ "Training: | | 0/? [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "source": [
- "type(data_ran)"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "BWZuJzqNg-ak",
- "outputId": "8076df63-2b15-4739-f5fe-9f703b68db6f"
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "52064c4ca7734cd9baea5a5d8e81a81a",
+ "version_major": 2,
+ "version_minor": 0
},
- "execution_count": 23,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "list"
- ]
- },
- "metadata": {},
- "execution_count": 23
- }
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "source": [
- "import random\n",
- "import numpy as np\n",
- "\n",
- "\n",
- "random.shuffle(data_ran)\n",
- "\n",
- "structures=[d[0] for d in data_ran[:15000]]\n",
- "targets=np.log10([d[1] for d in data_ran])\n",
- "\n",
- "print(structures[0],targets[0])"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "TG4g1Dp4hBhg",
- "outputId": "a3adbd5b-95c9-4ec9-8cd0-4c39136ea699"
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2e3d634584694485a3dc805dd4e6bb71",
+ "version_major": 2,
+ "version_minor": 0
},
- "execution_count": 24,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Full Formula (Li4 Ce4 O8)\n",
- "Reduced Formula: LiCeO2\n",
- "abc : 5.778710 5.859847 6.029586\n",
- "angles: 90.000000 90.000000 103.986129\n",
- "pbc : True True True\n",
- "Sites (16)\n",
- " # SP a b c\n",
- "--- ---- -------- -------- --------\n",
- " 0 Li 0.182502 0.662954 0.132604\n",
- " 1 Li 0.317498 0.337046 0.632604\n",
- " 2 Li 0.817498 0.337046 0.867396\n",
- " 3 Li 0.682502 0.662954 0.367396\n",
- " 4 Ce 0.303409 0.200495 0.071539\n",
- " 5 Ce 0.803409 0.200495 0.428461\n",
- " 6 Ce 0.696591 0.799505 0.928461\n",
- " 7 Ce 0.196591 0.799505 0.571539\n",
- " 8 O 0.986063 0.906145 0.246099\n",
- " 9 O 0.696592 0.43465 0.136137\n",
- " 10 O 0.513937 0.093855 0.746099\n",
- " 11 O 0.013937 0.093855 0.753901\n",
- " 12 O 0.303408 0.56535 0.863863\n",
- " 13 O 0.803408 0.56535 0.636137\n",
- " 14 O 0.196592 0.43465 0.363863\n",
- " 15 O 0.486063 0.906145 0.253901 2.0546896429499797\n"
- ]
- }
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "source": [
- "# get element types in the dataset\n",
- "elem_list = get_element_list(structures)\n",
- "# setup a graph converter\n",
- "converter = Structure2Graph(element_types=elem_list, cutoff=4.0)\n",
- "# convert the raw dataset into MEGNetDataset\n",
- "mp_dataset = MGLDataset(\n",
- " structures=structures,\n",
- " labels={\"bulk_modulus_kv\": targets},\n",
- " converter=converter,\n",
- ")"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Z-jazl2PhHsP",
- "outputId": "0044297b-6dc9-4f70-83f0-afb9c4c558d7"
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "6fbc5cb56b044b36b6ac6fa704a42509",
+ "version_major": 2,
+ "version_minor": 0
},
- "execution_count": 25,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "100%|██████████| 15000/15000 [00:24<00:00, 616.21it/s]\n"
- ]
- }
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "source": [
- "train_data, val_data, test_data = split_dataset(\n",
- " mp_dataset,\n",
- " frac_list=[0.6, 0.2, 0.2],\n",
- " shuffle=True,\n",
- " random_state=42,\n",
- ")\n",
- "train_loader, val_loader, test_loader = MGLDataLoader(\n",
- " train_data=train_data,\n",
- " val_data=val_data,\n",
- " test_data=test_data,\n",
- " batch_size=64,\n",
- " num_workers=0,\n",
- ")"
- ],
- "metadata": {
- "id": "QPyjAxGghK0Q"
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "f0a25dc24c19453ba9f3e84169914ed5",
+ "version_major": 2,
+ "version_minor": 0
},
- "execution_count": 26,
- "outputs": []
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "source": [
- "# setup the embedding layer for node attributes\n",
- "node_embed = torch.nn.Embedding(len(elem_list), 16)\n",
- "# define the bond expansion\n",
- "bond_expansion = BondExpansion(rbf_type=\"Gaussian\", initial=0.0, final=5.0, num_centers=100, width=0.5)\n",
- "\n",
- "# setup the architecture of MEGNet model\n",
- "model = MEGNet(\n",
- " dim_node_embedding=16,\n",
- " dim_edge_embedding=100,\n",
- " dim_state_embedding=2,\n",
- " nblocks=3,\n",
- " hidden_layer_sizes_input=(64, 32),\n",
- " hidden_layer_sizes_conv=(64, 64, 32),\n",
- " nlayers_set2set=1,\n",
- " niters_set2set=2,\n",
- " hidden_layer_sizes_output=(32, 16),\n",
- " is_classification=False,\n",
- " activation_type=\"softplus2\",\n",
- " bond_expansion=bond_expansion,\n",
- " #collate_fn=collate_fn, shivani - not needed now?\n",
- " cutoff=4.0,\n",
- " gauss_width=0.5,\n",
- ")\n",
- "\n",
- "# setup the MEGNetTrainer\n",
- "lit_module = ModelLightningModule(model=model)"
- ],
- "metadata": {
- "id": "pYGXwyZphQtd"
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "530114990c934b02b04ed88233a4cda3",
+ "version_major": 2,
+ "version_minor": 0
},
- "execution_count": 33,
- "outputs": []
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "source": [
- "logger = CSVLogger(\"logged\", name=\"MEGNet_training\")\n",
- "trainer = Trainer(max_epochs=5, accelerator=\"cpu\", logger=logger) #set to SMALL NUMBER FOR TESTING, PLEASE CHANGE.\n",
- "trainer.fit(model=lit_module, train_dataloaders=train_loader, val_dataloaders=val_loader)\n",
- "\n",
- "warnings.simplefilter(\"ignore\")"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 708,
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- },
- "id": "nqWWnzQUhoki",
- "outputId": "71988016-cb28-41b3-ea83-4400f135f481"
- },
- "execution_count": 40,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "INFO: GPU available: False, used: False\n",
- "INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n",
- "INFO: TPU available: False, using: 0 TPU cores\n",
- "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
- "INFO: HPU available: False, using: 0 HPUs\n",
- "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
- "INFO: \n",
- " | Name | Type | Params | Mode \n",
- "----------------------------------------------------\n",
- "0 | model | MEGNet | 189 K | train\n",
- "1 | mae | MeanAbsoluteError | 0 | train\n",
- "2 | rmse | MeanSquaredError | 0 | train\n",
- "----------------------------------------------------\n",
- "189 K Trainable params\n",
- "100 Non-trainable params\n",
- "189 K Total params\n",
- "0.758 Total estimated model params size (MB)\n",
- "109 Modules in train mode\n",
- "0 Modules in eval mode\n",
- "INFO:lightning.pytorch.callbacks.model_summary:\n",
- " | Name | Type | Params | Mode \n",
- "----------------------------------------------------\n",
- "0 | model | MEGNet | 189 K | train\n",
- "1 | mae | MeanAbsoluteError | 0 | train\n",
- "2 | rmse | MeanSquaredError | 0 | train\n",
- "----------------------------------------------------\n",
- "189 K Trainable params\n",
- "100 Non-trainable params\n",
- "189 K Total params\n",
- "0.758 Total estimated model params size (MB)\n",
- "109 Modules in train mode\n",
- "0 Modules in eval mode\n"
- ]
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "Sanity Checking: | | 0/? [00:00<?, ?it/s]"
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- },
- {
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- "metadata": {}
- },
- {
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- }
- },
- "metadata": {}
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "Validation: | | 0/? [00:00<?, ?it/s]"
- ],
- "application/vnd.jupyter.widget-view+json": {
- "version_major": 2,
- "version_minor": 0,
- "model_id": "530114990c934b02b04ed88233a4cda3"
- }
- },
- "metadata": {}
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "INFO: `Trainer.fit` stopped: `max_epochs=5` reached.\n",
- "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=5` reached.\n"
- ]
- }
- ]
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO: `Trainer.fit` stopped: `max_epochs=5` reached.\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=5` reached.\n"
+ ]
+ }
+ ],
+ "source": [
+ "logger = CSVLogger(\"logged\", name=\"MEGNet_training\")\n",
+ "trainer = Trainer(max_epochs=5, accelerator=\"cpu\", logger=logger) #set to SMALL NUMBER FOR TESTING, PLEASE CHANGE.\n",
+ "trainer.fit(model=lit_module, train_dataloaders=train_loader, val_dataloaders=val_loader)\n",
+ "\n",
+ "warnings.simplefilter(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 430
},
+ "id": "x2mTOHAGhqvE",
+ "outputId": "252e83de-6b95-4b75-ac17-cbc96a04a0cf"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "metrics = pd.read_csv(\"logged/MEGNet_training/version_0/metrics.csv\")\n",
- "metrics[\"train_MAE\"].dropna().plot()\n",
- "metrics[\"val_MAE\"].dropna().plot()\n",
- "\n",
- "_ = plt.legend()\n",
- "#plt.savefig(\"loss.jpg\")"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 430
- },
- "id": "x2mTOHAGhqvE",
- "outputId": "252e83de-6b95-4b75-ac17-cbc96a04a0cf"
- },
- "execution_count": 41,
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ],
- "image/png": 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\n"
- },
- "metadata": {}
- }
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "metrics = pd.read_csv(\"logged/MEGNet_training/version_0/metrics.csv\")\n",
+ "metrics[\"train_MAE\"].dropna().plot()\n",
+ "metrics[\"val_MAE\"].dropna().plot()\n",
+ "\n",
+ "_ = plt.legend()\n",
+ "#plt.savefig(\"loss.jpg\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 383
},
+ "id": "qZ0XV5e2jYXR",
+ "outputId": "ce1b9f10-d929-4db9-ace9-4c2801731f3b"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "metrics"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 383
- },
- "id": "qZ0XV5e2jYXR",
- "outputId": "ce1b9f10-d929-4db9-ace9-4c2801731f3b"
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "summary": "{\n \"name\": \"metrics\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"epoch\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 0,\n \"max\": 4,\n \"num_unique_values\": 5,\n \"samples\": [\n 1,\n 4,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"step\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 210,\n \"min\": 140,\n \"max\": 704,\n \"num_unique_values\": 5,\n \"samples\": [\n 281,\n 704,\n 422\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"train_MAE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"train_RMSE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"train_Total_Loss\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"val_MAE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"val_RMSE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"val_Total_Loss\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}",
+ "type": "dataframe",
+ "variable_name": "metrics"
},
- "execution_count": 42,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- " epoch step train_MAE train_RMSE train_Total_Loss val_MAE val_RMSE \\\n",
- "0 0 140 NaN NaN NaN NaN NaN \n",
- "1 0 140 NaN NaN NaN NaN NaN \n",
- "2 1 281 NaN NaN NaN NaN NaN \n",
- "3 1 281 NaN NaN NaN NaN NaN \n",
- "4 2 422 NaN NaN NaN NaN NaN \n",
- "5 2 422 NaN NaN NaN NaN NaN \n",
- "6 3 563 NaN NaN NaN NaN NaN \n",
- "7 3 563 NaN NaN NaN NaN NaN \n",
- "8 4 704 NaN NaN NaN NaN NaN \n",
- "9 4 704 NaN NaN NaN NaN NaN \n",
- "\n",
- " val_Total_Loss \n",
- "0 NaN \n",
- "1 NaN \n",
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+ ],
+ "text/plain": [
+ " epoch step train_MAE train_RMSE train_Total_Loss val_MAE val_RMSE \\\n",
+ "0 0 140 NaN NaN NaN NaN NaN \n",
+ "1 0 140 NaN NaN NaN NaN NaN \n",
+ "2 1 281 NaN NaN NaN NaN NaN \n",
+ "3 1 281 NaN NaN NaN NaN NaN \n",
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+ "5 2 422 NaN NaN NaN NaN NaN \n",
+ "6 3 563 NaN NaN NaN NaN NaN \n",
+ "7 3 563 NaN NaN NaN NaN NaN \n",
+ "8 4 704 NaN NaN NaN NaN NaN \n",
+ "9 4 704 NaN NaN NaN NaN NaN \n",
+ "\n",
+ " val_Total_Loss \n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 NaN \n",
+ "3 NaN \n",
+ "4 NaN \n",
+ "5 NaN \n",
+ "6 NaN \n",
+ "7 NaN \n",
+ "8 NaN \n",
+ "9 NaN "
]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "metrics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 233
},
+ "id": "XUzf_WX5jZb5",
+ "outputId": "be203ece-03f9-43e1-d66d-3f2213753479"
+ },
+ "outputs": [
{
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- "i=0\n",
- "prediction=np.zeros(len(test_data))\n",
- "for i in range(len(structures_test)):\n",
- " prediction[i]=model.predict_structure(structures_test[i])"
+ "ename": "NameError",
+ "evalue": "name 'structures_test' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-43-2431dbd08dbf>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstructures_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstructures_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'structures_test' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "i=0\n",
+ "prediction=np.zeros(len(test_data))\n",
+ "for i in range(len(structures_test)):\n",
+ " prediction[i]=model.predict_structure(structures_test[i])"
+ ]
+ },
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+ "Downloading jarvis_tools-2024.10.10-py2.py3-none-any.whl (4.2 MB)\n",
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+ "\u001b[?25hDownloading latexcodec-3.0.0-py3-none-any.whl (18 kB)\n",
+ "Downloading ruamel.yaml.clib-0.2.12-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (722 kB)\n",
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+ "\u001b[?25hInstalling collected packages: uncertainties, spglib, ruamel.yaml.clib, palettable, latexcodec, ruamel.yaml, pybtex, monty, pymatgen\n",
+ "Successfully installed latexcodec-3.0.0 monty-2024.10.21 palettable-3.3.3 pybtex-0.24.0 pymatgen-2024.10.29 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.12 spglib-2.5.0 uncertainties-3.2.2\n"
+ ]
+ }
+ ],
+ "source": [
+ " !pip3 install pymatgen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "510ELKTMF9Yu",
+ "outputId": "663be47b-f545-4f7b-ca73-d9cafaba2115"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- " !pip3 install pymatgen"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "q-pMYyCvF4Cq",
- "outputId": "ccf55b22-b816-4581-e1d6-13d8e2a9cdee"
- },
- "execution_count": 2,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Collecting pymatgen\n",
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- "\u001b[?25hInstalling collected packages: uncertainties, spglib, ruamel.yaml.clib, palettable, latexcodec, ruamel.yaml, pybtex, monty, pymatgen\n",
- "Successfully installed latexcodec-3.0.0 monty-2024.10.21 palettable-3.3.3 pybtex-0.24.0 pymatgen-2024.10.29 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.12 spglib-2.5.0 uncertainties-3.2.2\n"
- ]
- }
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Looking in links: https://data.dgl.ai/wheels/torch-2.1/repo.html\n",
+ "Collecting dgl\n",
+ " Downloading https://data.dgl.ai/wheels/torch-2.1/dgl-2.4.0-cp310-cp310-manylinux1_x86_64.whl (7.8 MB)\n",
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+ "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch<=2.4.0->dgl)\n",
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+ "Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch<=2.4.0->dgl)\n",
+ " Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+ "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch<=2.4.0->dgl)\n",
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+ "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch<=2.4.0->dgl)\n",
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+ " Downloading nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
+ "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch<=2.4.0->dgl)\n",
+ " Downloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
+ "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch<=2.4.0->dgl)\n",
+ " Downloading nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
+ "Collecting nvidia-nccl-cu12==2.20.5 (from torch<=2.4.0->dgl)\n",
+ " Downloading nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB)\n",
+ "Collecting nvidia-nvtx-cu12==12.1.105 (from torch<=2.4.0->dgl)\n",
+ " Downloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.7 kB)\n",
+ "Collecting triton==3.0.0 (from torch<=2.4.0->dgl)\n",
+ " Downloading triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.3 kB)\n",
+ "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch<=2.4.0->dgl) (12.6.77)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl) (2024.2)\n",
+ "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl) (2024.2)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->dgl) (1.16.0)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch<=2.4.0->dgl) (3.0.2)\n",
+ "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch<=2.4.0->dgl) (1.3.0)\n",
+ "Downloading torch-2.4.0-cp310-cp310-manylinux1_x86_64.whl (797.2 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m797.2/797.2 MB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m410.6/410.6 MB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m14.1/14.1 MB\u001b[0m \u001b[31m86.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m23.7/23.7 MB\u001b[0m \u001b[31m66.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m823.6/823.6 kB\u001b[0m \u001b[31m38.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m121.6/121.6 MB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m56.5/56.5 MB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m124.2/124.2 MB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m196.0/196.0 MB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m176.2/176.2 MB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (209.4 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m209.4/209.4 MB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hInstalling collected packages: triton, nvidia-nvtx-cu12, nvidia-nccl-cu12, nvidia-cusparse-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusolver-cu12, nvidia-cudnn-cu12, torch, dgl\n",
+ " Attempting uninstall: nvidia-nccl-cu12\n",
+ " Found existing installation: nvidia-nccl-cu12 2.23.4\n",
+ " Uninstalling nvidia-nccl-cu12-2.23.4:\n",
+ " Successfully uninstalled nvidia-nccl-cu12-2.23.4\n",
+ " Attempting uninstall: nvidia-cusparse-cu12\n",
+ " Found existing installation: nvidia-cusparse-cu12 12.5.4.2\n",
+ " Uninstalling nvidia-cusparse-cu12-12.5.4.2:\n",
+ " Successfully uninstalled nvidia-cusparse-cu12-12.5.4.2\n",
+ " Attempting uninstall: nvidia-curand-cu12\n",
+ " Found existing installation: nvidia-curand-cu12 10.3.7.77\n",
+ " Uninstalling nvidia-curand-cu12-10.3.7.77:\n",
+ " Successfully uninstalled nvidia-curand-cu12-10.3.7.77\n",
+ " Attempting uninstall: nvidia-cufft-cu12\n",
+ " Found existing installation: nvidia-cufft-cu12 11.3.0.4\n",
+ " Uninstalling nvidia-cufft-cu12-11.3.0.4:\n",
+ " Successfully uninstalled nvidia-cufft-cu12-11.3.0.4\n",
+ " Attempting uninstall: nvidia-cuda-runtime-cu12\n",
+ " Found existing installation: nvidia-cuda-runtime-cu12 12.6.77\n",
+ " Uninstalling nvidia-cuda-runtime-cu12-12.6.77:\n",
+ " Successfully uninstalled nvidia-cuda-runtime-cu12-12.6.77\n",
+ " Attempting uninstall: nvidia-cuda-cupti-cu12\n",
+ " Found existing installation: nvidia-cuda-cupti-cu12 12.6.80\n",
+ " Uninstalling nvidia-cuda-cupti-cu12-12.6.80:\n",
+ " Successfully uninstalled nvidia-cuda-cupti-cu12-12.6.80\n",
+ " Attempting uninstall: nvidia-cublas-cu12\n",
+ " Found existing installation: nvidia-cublas-cu12 12.6.3.3\n",
+ " Uninstalling nvidia-cublas-cu12-12.6.3.3:\n",
+ " Successfully uninstalled nvidia-cublas-cu12-12.6.3.3\n",
+ " Attempting uninstall: nvidia-cusolver-cu12\n",
+ " Found existing installation: nvidia-cusolver-cu12 11.7.1.2\n",
+ " Uninstalling nvidia-cusolver-cu12-11.7.1.2:\n",
+ " Successfully uninstalled nvidia-cusolver-cu12-11.7.1.2\n",
+ " Attempting uninstall: nvidia-cudnn-cu12\n",
+ " Found existing installation: nvidia-cudnn-cu12 9.5.1.17\n",
+ " Uninstalling nvidia-cudnn-cu12-9.5.1.17:\n",
+ " Successfully uninstalled nvidia-cudnn-cu12-9.5.1.17\n",
+ " Attempting uninstall: torch\n",
+ " Found existing installation: torch 2.5.0+cu121\n",
+ " Uninstalling torch-2.5.0+cu121:\n",
+ " Successfully uninstalled torch-2.5.0+cu121\n",
+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+ "torchaudio 2.5.0+cu121 requires torch==2.5.0, but you have torch 2.4.0 which is incompatible.\n",
+ "torchvision 0.20.0+cu121 requires torch==2.5.0, but you have torch 2.4.0 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0mSuccessfully installed dgl-2.4.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvtx-cu12-12.1.105 torch-2.4.0 triton-3.0.0\n"
+ ]
+ }
+ ],
+ "source": [
+ " !pip install dgl -f https://data.dgl.ai/wheels/torch-2.1/repo.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "saZDgwpVGAgq",
+ "outputId": "a16f7d96-a80b-4e72-ac6b-c68a9072455b"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- " !pip install dgl -f https://data.dgl.ai/wheels/torch-2.1/repo.html"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "510ELKTMF9Yu",
- "outputId": "663be47b-f545-4f7b-ca73-d9cafaba2115"
- },
- "execution_count": 3,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Looking in links: https://data.dgl.ai/wheels/torch-2.1/repo.html\n",
- "Collecting dgl\n",
- " Downloading https://data.dgl.ai/wheels/torch-2.1/dgl-2.4.0-cp310-cp310-manylinux1_x86_64.whl (7.8 MB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m17.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hRequirement already satisfied: networkx>=2.1 in /usr/local/lib/python3.10/dist-packages (from dgl) (3.4.2)\n",
- "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (1.26.4)\n",
- "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from dgl) (24.1)\n",
- "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from dgl) (2.2.2)\n",
- "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (5.9.5)\n",
- "Requirement already satisfied: pydantic>=2.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (2.9.2)\n",
- "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from dgl) (6.0.2)\n",
- "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (2.32.3)\n",
- "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (1.13.1)\n",
- "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from dgl) (4.66.6)\n",
- "Collecting torch<=2.4.0 (from dgl)\n",
- " Downloading torch-2.4.0-cp310-cp310-manylinux1_x86_64.whl.metadata (26 kB)\n",
- "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->dgl) (0.7.0)\n",
- "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->dgl) (2.23.4)\n",
- "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->dgl) (4.12.2)\n",
- "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (3.4.0)\n",
- "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (3.10)\n",
- "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (2.2.3)\n",
- "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (2024.8.30)\n",
- "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (3.16.1)\n",
- "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (1.13.1)\n",
- "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (3.1.4)\n",
- "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch<=2.4.0->dgl) (2024.10.0)\n",
- "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch<=2.4.0->dgl)\n",
- " Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
- "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch<=2.4.0->dgl)\n",
- " Downloading nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
- "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch<=2.4.0->dgl)\n",
- " Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
- "Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch<=2.4.0->dgl)\n",
- " Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
- "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch<=2.4.0->dgl)\n",
- " Downloading nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
- "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch<=2.4.0->dgl)\n",
- " Downloading nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
- "Collecting nvidia-curand-cu12==10.3.2.106 (from torch<=2.4.0->dgl)\n",
- " Downloading nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
- "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch<=2.4.0->dgl)\n",
- " Downloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
- "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch<=2.4.0->dgl)\n",
- " Downloading nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
- "Collecting nvidia-nccl-cu12==2.20.5 (from torch<=2.4.0->dgl)\n",
- " Downloading nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB)\n",
- "Collecting nvidia-nvtx-cu12==12.1.105 (from torch<=2.4.0->dgl)\n",
- " Downloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.7 kB)\n",
- "Collecting triton==3.0.0 (from torch<=2.4.0->dgl)\n",
- " Downloading triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.3 kB)\n",
- "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch<=2.4.0->dgl) (12.6.77)\n",
- "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl) (2.8.2)\n",
- "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl) (2024.2)\n",
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- "Downloading torch-2.4.0-cp310-cp310-manylinux1_x86_64.whl (797.2 MB)\n",
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- "\u001b[?25hDownloading nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m176.2/176.2 MB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hDownloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hDownloading triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (209.4 MB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m209.4/209.4 MB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hInstalling collected packages: triton, nvidia-nvtx-cu12, nvidia-nccl-cu12, nvidia-cusparse-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusolver-cu12, nvidia-cudnn-cu12, torch, dgl\n",
- " Attempting uninstall: nvidia-nccl-cu12\n",
- " Found existing installation: nvidia-nccl-cu12 2.23.4\n",
- " Uninstalling nvidia-nccl-cu12-2.23.4:\n",
- " Successfully uninstalled nvidia-nccl-cu12-2.23.4\n",
- " Attempting uninstall: nvidia-cusparse-cu12\n",
- " Found existing installation: nvidia-cusparse-cu12 12.5.4.2\n",
- " Uninstalling nvidia-cusparse-cu12-12.5.4.2:\n",
- " Successfully uninstalled nvidia-cusparse-cu12-12.5.4.2\n",
- " Attempting uninstall: nvidia-curand-cu12\n",
- " Found existing installation: nvidia-curand-cu12 10.3.7.77\n",
- " Uninstalling nvidia-curand-cu12-10.3.7.77:\n",
- " Successfully uninstalled nvidia-curand-cu12-10.3.7.77\n",
- " Attempting uninstall: nvidia-cufft-cu12\n",
- " Found existing installation: nvidia-cufft-cu12 11.3.0.4\n",
- " Uninstalling nvidia-cufft-cu12-11.3.0.4:\n",
- " Successfully uninstalled nvidia-cufft-cu12-11.3.0.4\n",
- " Attempting uninstall: nvidia-cuda-runtime-cu12\n",
- " Found existing installation: nvidia-cuda-runtime-cu12 12.6.77\n",
- " Uninstalling nvidia-cuda-runtime-cu12-12.6.77:\n",
- " Successfully uninstalled nvidia-cuda-runtime-cu12-12.6.77\n",
- " Attempting uninstall: nvidia-cuda-cupti-cu12\n",
- " Found existing installation: nvidia-cuda-cupti-cu12 12.6.80\n",
- " Uninstalling nvidia-cuda-cupti-cu12-12.6.80:\n",
- " Successfully uninstalled nvidia-cuda-cupti-cu12-12.6.80\n",
- " Attempting uninstall: nvidia-cublas-cu12\n",
- " Found existing installation: nvidia-cublas-cu12 12.6.3.3\n",
- " Uninstalling nvidia-cublas-cu12-12.6.3.3:\n",
- " Successfully uninstalled nvidia-cublas-cu12-12.6.3.3\n",
- " Attempting uninstall: nvidia-cusolver-cu12\n",
- " Found existing installation: nvidia-cusolver-cu12 11.7.1.2\n",
- " Uninstalling nvidia-cusolver-cu12-11.7.1.2:\n",
- " Successfully uninstalled nvidia-cusolver-cu12-11.7.1.2\n",
- " Attempting uninstall: nvidia-cudnn-cu12\n",
- " Found existing installation: nvidia-cudnn-cu12 9.5.1.17\n",
- " Uninstalling nvidia-cudnn-cu12-9.5.1.17:\n",
- " Successfully uninstalled nvidia-cudnn-cu12-9.5.1.17\n",
- " Attempting uninstall: torch\n",
- " Found existing installation: torch 2.5.0+cu121\n",
- " Uninstalling torch-2.5.0+cu121:\n",
- " Successfully uninstalled torch-2.5.0+cu121\n",
- "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
- "torchaudio 2.5.0+cu121 requires torch==2.5.0, but you have torch 2.4.0 which is incompatible.\n",
- "torchvision 0.20.0+cu121 requires torch==2.5.0, but you have torch 2.4.0 which is incompatible.\u001b[0m\u001b[31m\n",
- "\u001b[0mSuccessfully installed dgl-2.4.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvtx-cu12-12.1.105 torch-2.4.0 triton-3.0.0\n"
- ]
- }
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting matgl\n",
+ " Downloading matgl-1.1.3-py3-none-any.whl.metadata (16 kB)\n",
+ "Collecting ase (from matgl)\n",
+ " Downloading ase-3.23.0-py3-none-any.whl.metadata (3.8 kB)\n",
+ "Requirement already satisfied: dgl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from matgl) (2.4.0)\n",
+ "Requirement already satisfied: pymatgen in /usr/local/lib/python3.10/dist-packages (from matgl) (2024.10.29)\n",
+ "Collecting lightning (from matgl)\n",
+ " Downloading lightning-2.4.0-py3-none-any.whl.metadata (38 kB)\n",
+ "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from matgl) (2.4.0)\n",
+ "Requirement already satisfied: pydantic in /usr/local/lib/python3.10/dist-packages (from matgl) (2.9.2)\n",
+ "Collecting torchdata<0.8.0 (from matgl)\n",
+ " Downloading torchdata-0.7.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
+ "Requirement already satisfied: networkx>=2.1 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (3.4.2)\n",
+ "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (1.26.4)\n",
+ "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (24.1)\n",
+ "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (2.2.2)\n",
+ "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (5.9.5)\n",
+ "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (6.0.2)\n",
+ "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (2.32.3)\n",
+ "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (1.13.1)\n",
+ "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from dgl>=2.0.0->matgl) (4.66.6)\n",
+ "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic->matgl) (0.7.0)\n",
+ "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic->matgl) (2.23.4)\n",
+ "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic->matgl) (4.12.2)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (3.16.1)\n",
+ "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (1.13.1)\n",
+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (3.1.4)\n",
+ "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (2024.10.0)\n",
+ "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (9.1.0.70)\n",
+ "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.3.1)\n",
+ "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (11.0.2.54)\n",
+ "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (10.3.2.106)\n",
+ "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (11.4.5.107)\n",
+ "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.0.106)\n",
+ "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (2.20.5)\n",
+ "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (12.1.105)\n",
+ "Requirement already satisfied: triton==3.0.0 in /usr/local/lib/python3.10/dist-packages (from torch->matgl) (3.0.0)\n",
+ "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->matgl) (12.6.77)\n",
+ "Requirement already satisfied: urllib3>=1.25 in /usr/local/lib/python3.10/dist-packages (from torchdata<0.8.0->matgl) (2.2.3)\n",
+ "Requirement already satisfied: matplotlib>=3.3.4 in /usr/local/lib/python3.10/dist-packages (from ase->matgl) (3.8.0)\n",
+ "Collecting lightning-utilities<2.0,>=0.10.0 (from lightning->matgl)\n",
+ " Downloading lightning_utilities-0.11.8-py3-none-any.whl.metadata (5.2 kB)\n",
+ "Collecting torchmetrics<3.0,>=0.7.0 (from lightning->matgl)\n",
+ " Downloading torchmetrics-1.5.1-py3-none-any.whl.metadata (20 kB)\n",
+ "Collecting pytorch-lightning (from lightning->matgl)\n",
+ " Downloading pytorch_lightning-2.4.0-py3-none-any.whl.metadata (21 kB)\n",
+ "Requirement already satisfied: joblib>=1 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (1.4.2)\n",
+ "Requirement already satisfied: monty>=2024.7.29 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (2024.10.21)\n",
+ "Requirement already satisfied: palettable>=3.3.3 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (3.3.3)\n",
+ "Requirement already satisfied: plotly>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (5.24.1)\n",
+ "Requirement already satisfied: pybtex>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (0.24.0)\n",
+ "Requirement already satisfied: ruamel.yaml>=0.17.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (0.18.6)\n",
+ "Requirement already satisfied: spglib>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (2.5.0)\n",
+ "Requirement already satisfied: tabulate>=0.9 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (0.9.0)\n",
+ "Requirement already satisfied: uncertainties>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from pymatgen->matgl) (3.2.2)\n",
+ "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (3.10.10)\n",
+ "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from lightning-utilities<2.0,>=0.10.0->lightning->matgl) (75.1.0)\n",
+ "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (1.3.0)\n",
+ "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (0.12.1)\n",
+ "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (4.54.1)\n",
+ "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (1.4.7)\n",
+ "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (10.4.0)\n",
+ "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (3.2.0)\n",
+ "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase->matgl) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->dgl>=2.0.0->matgl) (2024.2)\n",
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+ "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (1.3.1)\n",
+ "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (24.2.0)\n",
+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (1.5.0)\n",
+ "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (6.1.0)\n",
+ "Requirement already satisfied: yarl<2.0,>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (1.17.0)\n",
+ "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (4.0.3)\n",
+ "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from yarl<2.0,>=1.12.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning->matgl) (0.2.0)\n",
+ "Downloading matgl-1.1.3-py3-none-any.whl (223 kB)\n",
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+ "\u001b[?25hDownloading torchdata-0.7.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.7 MB)\n",
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+ "\u001b[?25hDownloading ase-3.23.0-py3-none-any.whl (2.9 MB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading lightning-2.4.0-py3-none-any.whl (810 kB)\n",
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+ "\u001b[?25hDownloading lightning_utilities-0.11.8-py3-none-any.whl (26 kB)\n",
+ "Downloading torchmetrics-1.5.1-py3-none-any.whl (890 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m890.6/890.6 kB\u001b[0m \u001b[31m28.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading pytorch_lightning-2.4.0-py3-none-any.whl (815 kB)\n",
+ "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m815.2/815.2 kB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hInstalling collected packages: lightning-utilities, ase, torchmetrics, torchdata, pytorch-lightning, lightning, matgl\n",
+ "Successfully installed ase-3.23.0 lightning-2.4.0 lightning-utilities-0.11.8 matgl-1.1.3 pytorch-lightning-2.4.0 torchdata-0.7.1 torchmetrics-1.5.1\n"
+ ]
+ }
+ ],
+ "source": [
+ " !pip3 install matgl"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "uHFBr-yJGC8G",
+ "outputId": "74eed14d-d004-4666-95f5-57d3411d045c"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- " !pip3 install matgl"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "saZDgwpVGAgq",
- "outputId": "a16f7d96-a80b-4e72-ac6b-c68a9072455b"
- },
- "execution_count": 4,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Collecting matgl\n",
- " Downloading matgl-1.1.3-py3-none-any.whl.metadata (16 kB)\n",
- "Collecting ase (from matgl)\n",
- " Downloading ase-3.23.0-py3-none-any.whl.metadata (3.8 kB)\n",
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- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m4.7/4.7 MB\u001b[0m \u001b[31m25.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hDownloading ase-3.23.0-py3-none-any.whl (2.9 MB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hDownloading lightning-2.4.0-py3-none-any.whl (810 kB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m811.0/811.0 kB\u001b[0m \u001b[31m30.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hDownloading lightning_utilities-0.11.8-py3-none-any.whl (26 kB)\n",
- "Downloading torchmetrics-1.5.1-py3-none-any.whl (890 kB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m890.6/890.6 kB\u001b[0m \u001b[31m28.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hDownloading pytorch_lightning-2.4.0-py3-none-any.whl (815 kB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m815.2/815.2 kB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hInstalling collected packages: lightning-utilities, ase, torchmetrics, torchdata, pytorch-lightning, lightning, matgl\n",
- "Successfully installed ase-3.23.0 lightning-2.4.0 lightning-utilities-0.11.8 matgl-1.1.3 pytorch-lightning-2.4.0 torchdata-0.7.1 torchmetrics-1.5.1\n"
- ]
- }
- ]
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "DGL backend not selected or invalid. Assuming PyTorch for now.\n"
+ ]
},
{
- "cell_type": "code",
- "source": [
- "from __future__ import annotations\n",
- "\n",
- "import os\n",
- "import shutil\n",
- "import warnings\n",
- "import zipfile\n",
- "import matgl\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import pandas as pd\n",
- "import pytorch_lightning as pl\n",
- "import torch\n",
- "import pickle\n",
- "import numpy as np\n",
- "from dgl.data.utils import split_dataset\n",
- "from pymatgen.core import Structure\n",
- "from pytorch_lightning.loggers import CSVLogger\n",
- "from lightning.pytorch import Trainer\n",
- "from tqdm import tqdm\n",
- "\n",
- "from matgl.ext.pymatgen import Structure2Graph, get_element_list\n",
- "from matgl.graph.data import MGLDataset, MGLDataLoader #collate_fn. - shivani i don't think you need this as num_workers=0\n",
- "from matgl.layers import BondExpansion\n",
- "from matgl.models import MEGNet\n",
- "from matgl.utils.io import RemoteFile\n",
- "from matgl.utils.training import ModelLightningModule\n",
- "\n",
- "# To suppress warnings for clearer output\n",
- "warnings.simplefilter(\"ignore\")"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "uHFBr-yJGC8G",
- "outputId": "74eed14d-d004-4666-95f5-57d3411d045c"
- },
- "execution_count": 5,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "DGL backend not selected or invalid. Assuming PyTorch for now.\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable. Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n"
- ]
- }
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable. Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from __future__ import annotations\n",
+ "\n",
+ "import os\n",
+ "import shutil\n",
+ "import warnings\n",
+ "import zipfile\n",
+ "import matgl\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import pytorch_lightning as pl\n",
+ "import torch\n",
+ "import pickle\n",
+ "import numpy as np\n",
+ "from dgl.data.utils import split_dataset\n",
+ "from pymatgen.core import Structure\n",
+ "from pytorch_lightning.loggers import CSVLogger\n",
+ "from lightning.pytorch import Trainer\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "from matgl.ext.pymatgen import Structure2Graph, get_element_list\n",
+ "from matgl.graph.data import MGLDataset, MGLDataLoader #collate_fn. - shivani i don't think you need this as num_workers=0\n",
+ "from matgl.layers import BondExpansion\n",
+ "from matgl.models import MEGNet\n",
+ "from matgl.utils.io import RemoteFile\n",
+ "from matgl.utils.training import ModelLightningModule\n",
+ "\n",
+ "# To suppress warnings for clearer output\n",
+ "warnings.simplefilter(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "WfEpSLibGHh2",
+ "outputId": "1e900a8f-9920-4d5c-fb25-cd459c23ead3"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Obtaining 3D dataset 76k ...\n",
+ "Reference:https://www.nature.com/articles/s41524-020-00440-1\n",
+ "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 40.8M/40.8M [00:02<00:00, 20.0MiB/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading the zipfile...\n",
+ "Loading completed.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from jarvis.db.figshare import data\n",
+ "\n",
+ "dft_3d = data('dft_3d')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "2wNH5_F6GM_E",
+ "outputId": "9f14897d-e01c-4f12-f504-e06724d18eb2"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['jid', 'spg_number', 'spg_symbol', 'formula', 'formation_energy_peratom', 'func', 'optb88vdw_bandgap', 'atoms', 'slme', 'magmom_oszicar', 'spillage', 'elastic_tensor', 'effective_masses_300K', 'kpoint_length_unit', 'maxdiff_mesh', 'maxdiff_bz', 'encut', 'optb88vdw_total_energy', 'epsx', 'epsy', 'epsz', 'mepsx', 'mepsy', 'mepsz', 'modes', 'magmom_outcar', 'max_efg', 'avg_elec_mass', 'avg_hole_mass', 'icsd', 'dfpt_piezo_max_eij', 'dfpt_piezo_max_dij', 'dfpt_piezo_max_dielectric', 'dfpt_piezo_max_dielectric_electronic', 'dfpt_piezo_max_dielectric_ionic', 'max_ir_mode', 'min_ir_mode', 'n-Seebeck', 'p-Seebeck', 'n-powerfact', 'p-powerfact', 'ncond', 'pcond', 'nkappa', 'pkappa', 'ehull', 'Tc_supercon', 'dimensionality', 'efg', 'xml_data_link', 'typ', 'exfoliation_energy', 'spg', 'crys', 'density', 'poisson', 'raw_files', 'nat', 'bulk_modulus_kv', 'shear_modulus_gv', 'mbj_bandgap', 'hse_gap', 'reference', 'search'])"
]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dft_3d[0].keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "id": "6HEIHJvTGPDs"
+ },
+ "outputs": [],
+ "source": [
+ " ## Let's make a dataframe from this:\n",
+ "import pandas as pd\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 429
},
+ "id": "RT5c9__tGRKC",
+ "outputId": "ca6651ee-1d6d-4930-ff8d-aa88babf8e08"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "from jarvis.db.figshare import data\n",
- "\n",
- "dft_3d = data('dft_3d')"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "WfEpSLibGHh2",
- "outputId": "1e900a8f-9920-4d5c-fb25-cd459c23ead3"
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "dataframe",
+ "variable_name": "df"
},
- "execution_count": 6,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Obtaining 3D dataset 76k ...\n",
- "Reference:https://www.nature.com/articles/s41524-020-00440-1\n",
- "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "100%|██████████| 40.8M/40.8M [00:02<00:00, 20.0MiB/s]\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Loading the zipfile...\n",
- "Loading completed.\n"
- ]
- }
+ "text/html": [
+ "\n",
+ " <div id=\"df-9e94c52f-4b6c-4f88-81b3-0d74cccf7eec\" class=\"colab-df-container\">\n",
+ " <div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>jid</th>\n",
+ " <th>spg_number</th>\n",
+ " <th>spg_symbol</th>\n",
+ " <th>formula</th>\n",
+ " <th>formation_energy_peratom</th>\n",
+ " <th>func</th>\n",
+ " <th>optb88vdw_bandgap</th>\n",
+ " <th>atoms</th>\n",
+ " <th>slme</th>\n",
+ " <th>magmom_oszicar</th>\n",
+ " <th>...</th>\n",
+ " <th>density</th>\n",
+ " <th>poisson</th>\n",
+ " <th>raw_files</th>\n",
+ " <th>nat</th>\n",
+ " <th>bulk_modulus_kv</th>\n",
+ " <th>shear_modulus_gv</th>\n",
+ " <th>mbj_bandgap</th>\n",
+ " <th>hse_gap</th>\n",
+ " <th>reference</th>\n",
+ " <th>search</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>JVASP-90856</td>\n",
+ " <td>129</td>\n",
+ " <td>P4/nmm</td>\n",
+ " <td>TiCuSiAs</td>\n",
+ " <td>-0.42762</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[3.566933224304235, 0.0, -0.0...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.956</td>\n",
+ " <td>na</td>\n",
+ " <td>[]</td>\n",
+ " <td>8</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-1080455</td>\n",
+ " <td>-As-Cu-Si-Ti</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>JVASP-86097</td>\n",
+ " <td>221</td>\n",
+ " <td>Pm-3m</td>\n",
+ " <td>DyB6</td>\n",
+ " <td>-0.41596</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[4.089078911208881, 0.0, 0.0]...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.522</td>\n",
+ " <td>na</td>\n",
+ " <td>[OPT-LOPTICS,JVASP-86097.zip,https://ndownload...</td>\n",
+ " <td>7</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-568319</td>\n",
+ " <td>-B-Dy</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>JVASP-64906</td>\n",
+ " <td>119</td>\n",
+ " <td>I-4m2</td>\n",
+ " <td>Be2OsRu</td>\n",
+ " <td>0.04847</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[-1.833590720595598, 1.833590...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>10.960</td>\n",
+ " <td>na</td>\n",
+ " <td>[OPT-LOPTICS,JVASP-64906.zip,https://ndownload...</td>\n",
+ " <td>4</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>auid-3eaf68dd483bf4f4</td>\n",
+ " <td>-Be-Os-Ru</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>JVASP-98225</td>\n",
+ " <td>14</td>\n",
+ " <td>P2_1/c</td>\n",
+ " <td>KBi</td>\n",
+ " <td>-0.44140</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.472</td>\n",
+ " <td>{'lattice_mat': [[7.2963518353359165, 0.0, 0.0...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.145</td>\n",
+ " <td>na</td>\n",
+ " <td>[]</td>\n",
+ " <td>32</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-31104</td>\n",
+ " <td>-Bi-K</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>JVASP-10</td>\n",
+ " <td>164</td>\n",
+ " <td>P-3m1</td>\n",
+ " <td>VSe2</td>\n",
+ " <td>-0.71026</td>\n",
+ " <td>OptB88vdW</td>\n",
+ " <td>0.000</td>\n",
+ " <td>{'lattice_mat': [[1.6777483798834445, -2.90594...</td>\n",
+ " <td>na</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>5.718</td>\n",
+ " <td>0.23</td>\n",
+ " <td>[FD-ELAST,JVASP-10.zip,https://ndownloader.fig...</td>\n",
+ " <td>3</td>\n",
+ " <td>48.79</td>\n",
+ " <td>33.05</td>\n",
+ " <td>0.0</td>\n",
+ " <td>na</td>\n",
+ " <td>mp-694</td>\n",
+ " <td>-Se-V</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 64 columns</p>\n",
+ "</div>\n",
+ " <div class=\"colab-df-buttons\">\n",
+ "\n",
+ " <div class=\"colab-df-container\">\n",
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e94c52f-4b6c-4f88-81b3-0d74cccf7eec')\"\n",
+ " title=\"Convert this dataframe to an interactive table.\"\n",
+ " style=\"display:none;\">\n",
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+ " </svg>\n",
+ " </button>\n",
+ "\n",
+ " <style>\n",
+ " .colab-df-container {\n",
+ " display:flex;\n",
+ " gap: 12px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert {\n",
+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: #1967D2;\n",
+ " height: 32px;\n",
+ " padding: 0 0 0 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert:hover {\n",
+ " background-color: #E2EBFA;\n",
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: #174EA6;\n",
+ " }\n",
+ "\n",
+ " .colab-df-buttons div {\n",
+ " margin-bottom: 4px;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert {\n",
+ " background-color: #3B4455;\n",
+ " fill: #D2E3FC;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert:hover {\n",
+ " background-color: #434B5C;\n",
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ "\n",
+ " <script>\n",
+ " const buttonEl =\n",
+ " document.querySelector('#df-9e94c52f-4b6c-4f88-81b3-0d74cccf7eec button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-9e94c52f-4b6c-4f88-81b3-0d74cccf7eec');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ "\n",
+ "\n",
+ "<div id=\"df-66c438ee-b30e-48e1-808e-7b2da6583b0f\">\n",
+ " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-66c438ee-b30e-48e1-808e-7b2da6583b0f')\"\n",
+ " title=\"Suggest charts\"\n",
+ " style=\"display:none;\">\n",
+ "\n",
+ "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+ " width=\"24px\">\n",
+ " <g>\n",
+ " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+ " </g>\n",
+ "</svg>\n",
+ " </button>\n",
+ "\n",
+ "<style>\n",
+ " .colab-df-quickchart {\n",
+ " --bg-color: #E8F0FE;\n",
+ " --fill-color: #1967D2;\n",
+ " --hover-bg-color: #E2EBFA;\n",
+ " --hover-fill-color: #174EA6;\n",
+ " --disabled-fill-color: #AAA;\n",
+ " --disabled-bg-color: #DDD;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-quickchart {\n",
+ " --bg-color: #3B4455;\n",
+ " --fill-color: #D2E3FC;\n",
+ " --hover-bg-color: #434B5C;\n",
+ " --hover-fill-color: #FFFFFF;\n",
+ " --disabled-bg-color: #3B4455;\n",
+ " --disabled-fill-color: #666;\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart {\n",
+ " background-color: var(--bg-color);\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: var(--fill-color);\n",
+ " height: 32px;\n",
+ " padding: 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart:hover {\n",
+ " background-color: var(--hover-bg-color);\n",
+ " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: var(--button-hover-fill-color);\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart-complete:disabled,\n",
+ " .colab-df-quickchart-complete:disabled:hover {\n",
+ " background-color: var(--disabled-bg-color);\n",
+ " fill: var(--disabled-fill-color);\n",
+ " box-shadow: none;\n",
+ " }\n",
+ "\n",
+ " .colab-df-spinner {\n",
+ " border: 2px solid var(--fill-color);\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " animation:\n",
+ " spin 1s steps(1) infinite;\n",
+ " }\n",
+ "\n",
+ " @keyframes spin {\n",
+ " 0% {\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " border-left-color: var(--fill-color);\n",
+ " }\n",
+ " 20% {\n",
+ " border-color: transparent;\n",
+ " border-left-color: var(--fill-color);\n",
+ " border-top-color: var(--fill-color);\n",
+ " }\n",
+ " 30% {\n",
+ " border-color: transparent;\n",
+ " border-left-color: var(--fill-color);\n",
+ " border-top-color: var(--fill-color);\n",
+ " border-right-color: var(--fill-color);\n",
+ " }\n",
+ " 40% {\n",
+ " border-color: transparent;\n",
+ " border-right-color: var(--fill-color);\n",
+ " border-top-color: var(--fill-color);\n",
+ " }\n",
+ " 60% {\n",
+ " border-color: transparent;\n",
+ " border-right-color: var(--fill-color);\n",
+ " }\n",
+ " 80% {\n",
+ " border-color: transparent;\n",
+ " border-right-color: var(--fill-color);\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " }\n",
+ " 90% {\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " }\n",
+ " }\n",
+ "</style>\n",
+ "\n",
+ " <script>\n",
+ " async function quickchart(key) {\n",
+ " const quickchartButtonEl =\n",
+ " document.querySelector('#' + key + ' button');\n",
+ " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
+ " quickchartButtonEl.classList.add('colab-df-spinner');\n",
+ " try {\n",
+ " const charts = await google.colab.kernel.invokeFunction(\n",
+ " 'suggestCharts', [key], {});\n",
+ " } catch (error) {\n",
+ " console.error('Error during call to suggestCharts:', error);\n",
+ " }\n",
+ " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+ " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+ " }\n",
+ " (() => {\n",
+ " let quickchartButtonEl =\n",
+ " document.querySelector('#df-66c438ee-b30e-48e1-808e-7b2da6583b0f button');\n",
+ " quickchartButtonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ " })();\n",
+ " </script>\n",
+ "</div>\n",
+ "\n",
+ " </div>\n",
+ " </div>\n"
+ ],
+ "text/plain": [
+ " jid spg_number spg_symbol formula formation_energy_peratom \\\n",
+ "0 JVASP-90856 129 P4/nmm TiCuSiAs -0.42762 \n",
+ "1 JVASP-86097 221 Pm-3m DyB6 -0.41596 \n",
+ "2 JVASP-64906 119 I-4m2 Be2OsRu 0.04847 \n",
+ "3 JVASP-98225 14 P2_1/c KBi -0.44140 \n",
+ "4 JVASP-10 164 P-3m1 VSe2 -0.71026 \n",
+ "\n",
+ " func optb88vdw_bandgap \\\n",
+ "0 OptB88vdW 0.000 \n",
+ "1 OptB88vdW 0.000 \n",
+ "2 OptB88vdW 0.000 \n",
+ "3 OptB88vdW 0.472 \n",
+ "4 OptB88vdW 0.000 \n",
+ "\n",
+ " atoms slme magmom_oszicar ... \\\n",
+ "0 {'lattice_mat': [[3.566933224304235, 0.0, -0.0... na 0.0 ... \n",
+ "1 {'lattice_mat': [[4.089078911208881, 0.0, 0.0]... na 0.0 ... \n",
+ "2 {'lattice_mat': [[-1.833590720595598, 1.833590... na 0.0 ... \n",
+ "3 {'lattice_mat': [[7.2963518353359165, 0.0, 0.0... na 0.0 ... \n",
+ "4 {'lattice_mat': [[1.6777483798834445, -2.90594... na 0.0 ... \n",
+ "\n",
+ " density poisson raw_files nat \\\n",
+ "0 5.956 na [] 8 \n",
+ "1 5.522 na [OPT-LOPTICS,JVASP-86097.zip,https://ndownload... 7 \n",
+ "2 10.960 na [OPT-LOPTICS,JVASP-64906.zip,https://ndownload... 4 \n",
+ "3 5.145 na [] 32 \n",
+ "4 5.718 0.23 [FD-ELAST,JVASP-10.zip,https://ndownloader.fig... 3 \n",
+ "\n",
+ " bulk_modulus_kv shear_modulus_gv mbj_bandgap hse_gap \\\n",
+ "0 na na na na \n",
+ "1 na na na na \n",
+ "2 na na na na \n",
+ "3 na na na na \n",
+ "4 48.79 33.05 0.0 na \n",
+ "\n",
+ " reference search \n",
+ "0 mp-1080455 -As-Cu-Si-Ti \n",
+ "1 mp-568319 -B-Dy \n",
+ "2 auid-3eaf68dd483bf4f4 -Be-Os-Ru \n",
+ "3 mp-31104 -Bi-K \n",
+ "4 mp-694 -Se-V \n",
+ "\n",
+ "[5 rows x 64 columns]"
]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df=pd.DataFrame(dft_3d)\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "FTsZSTDbGUbH",
+ "outputId": "3a024410-29ed-442a-c405-a90b5985da09"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "dft_3d[0].keys()"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "2wNH5_F6GM_E",
- "outputId": "9f14897d-e01c-4f12-f504-e06724d18eb2"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "jid 75993\n",
+ "spg_number 75993\n",
+ "spg_symbol 75993\n",
+ "formula 75993\n",
+ "formation_energy_peratom 75993\n",
+ "func 75993\n",
+ "optb88vdw_bandgap 75993\n",
+ "atoms 75993\n",
+ "slme 9770\n",
+ "magmom_oszicar 71320\n",
+ "spillage 11377\n",
+ "elastic_tensor 25513\n",
+ "effective_masses_300K 75993\n",
+ "kpoint_length_unit 75671\n",
+ "maxdiff_mesh 5861\n",
+ "maxdiff_bz 5861\n",
+ "encut 75670\n",
+ "optb88vdw_total_energy 75993\n",
+ "epsx 52168\n",
+ "epsy 52168\n",
+ "epsz 52168\n",
+ "mepsx 18293\n",
+ "mepsy 18293\n",
+ "mepsz 18293\n",
+ "modes 13910\n",
+ "magmom_outcar 74261\n",
+ "max_efg 11871\n",
+ "avg_elec_mass 17645\n",
+ "avg_hole_mass 17645\n",
+ "icsd 75993\n",
+ "dfpt_piezo_max_eij 4799\n",
+ "dfpt_piezo_max_dij 3347\n",
+ "dfpt_piezo_max_dielectric 4706\n",
+ "dfpt_piezo_max_dielectric_electronic 4809\n",
+ "dfpt_piezo_max_dielectric_ionic 4809\n",
+ "max_ir_mode 4805\n",
+ "min_ir_mode 4809\n",
+ "n-Seebeck 23218\n",
+ "p-Seebeck 23218\n",
+ "n-powerfact 23218\n",
+ "p-powerfact 23218\n",
+ "ncond 23218\n",
+ "pcond 23218\n",
+ "nkappa 23218\n",
+ "pkappa 23218\n",
+ "ehull 75993\n",
+ "Tc_supercon 1058\n",
+ "dimensionality 75560\n",
+ "efg 75993\n",
+ "xml_data_link 75993\n",
+ "typ 75993\n",
+ "exfoliation_energy 813\n",
+ "spg 75993\n",
+ "crys 75993\n",
+ "density 75993\n",
+ "poisson 23597\n",
+ "raw_files 75993\n",
+ "nat 75993\n",
+ "bulk_modulus_kv 23824\n",
+ "shear_modulus_gv 23824\n",
+ "mbj_bandgap 19805\n",
+ "hse_gap 56\n",
+ "reference 75993\n",
+ "search 75993\n"
+ ]
+ }
+ ],
+ "source": [
+ "## Count number of entries for each property\n",
+ "for i in df.columns.values:\n",
+ " val=df[i].replace('na',np.nan).dropna().values\n",
+ " print(i,len(val))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "rW4KEnICGVxE"
+ },
+ "outputs": [],
+ "source": [
+ "from jarvis.core.atoms import Atoms\n",
+ "bm=df[df.bulk_modulus_kv != 'na']\n",
+ "data = [(Atoms.from_dict(bm.iloc[i]['atoms']).pymatgen_converter(), bm.iloc[i].bulk_modulus_kv) for i in range(len(bm))]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "id": "0gQUS5rQGaK5"
+ },
+ "outputs": [],
+ "source": [
+ "import itertools\n",
+ "def get_stoichiometry(elements):\n",
+ " return [(g[0], len(list(g[1]))) for g in itertools.groupby(elements)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "YaMVVrguGe2s",
+ "outputId": "25dcd209-e980-4a20-c650-eb17046dda62"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 23824/23824 [00:36<00:00, 656.64it/s] \n"
+ ]
+ }
+ ],
+ "source": [
+ " ## Use all the material dataset for training the bulk modulus\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "stoichs=[] #stoichiometry\n",
+ "bulk=[] #only include positive bulk modulus\n",
+ "\n",
+ "for i in tqdm(range(len(bm))):\n",
+ " if (bm.iloc[i]['bulk_modulus_kv'])>1:\n",
+ " stoichs.append(Atoms.from_dict(bm.iloc[i]['atoms']).pymatgen_converter())\n",
+ " bulk.append(bm.iloc[i]['bulk_modulus_kv'])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "id": "0lxmfaHEHhs6"
+ },
+ "outputs": [],
+ "source": [
+ "data_ran=list(zip(stoichs,bulk))\n",
+ "#write out the dataset, to train later\n",
+ "import pickle\n",
+ "with open('data_ran.pickle', 'wb') as f:\n",
+ " pickle.dump(data_ran, f)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "id": "dz2y9oNGH-rO"
+ },
+ "outputs": [],
+ "source": [
+ " #read in the dataset\n",
+ "data_ran=pd.read_pickle('./data_ran.pickle')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "IllZhvMxHh0x",
+ "outputId": "9d8e5d95-ff3b-4b06-d674-ac15c663fe6e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Full Formula (Al2 O1)\n",
+ "Reduced Formula: Al2O\n",
+ "abc : 2.691974 2.821216 5.955222\n",
+ "angles: 90.000000 90.000000 90.000000\n",
+ "pbc : True True True\n",
+ "Sites (3)\n",
+ " # SP a b c\n",
+ "--- ---- --------- --- --------\n",
+ " 0 Al -0.033312 0 0.757299\n",
+ " 1 Al -0.033312 0 0.242701\n",
+ " 2 O 0.466623 0 0 1.757547853469244\n"
+ ]
+ }
+ ],
+ "source": [
+ "import random\n",
+ "\n",
+ "random.shuffle(data_ran)\n",
+ "\n",
+ "structures=[d[0] for d in data_ran]\n",
+ "targets=np.log10([d[1] for d in data_ran])\n",
+ "\n",
+ "print(structures[0],targets[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gkBUklhmIeOb",
+ "outputId": "13cc0804-8a6b-4a18-9153-228fe781ab5b"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 23173/23173 [00:47<00:00, 483.02it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# get element types in the dataset\n",
+ "elem_list = get_element_list(structures)\n",
+ "# setup a graph converter\n",
+ "converter = Structure2Graph(element_types=elem_list, cutoff=4.0)\n",
+ "# convert the raw dataset into MEGNetDataset\n",
+ "mp_dataset = MGLDataset(\n",
+ " structures=structures,\n",
+ " labels={\"bulk_modulus_kv\": targets},\n",
+ " converter=converter,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "id": "Tk7mjkwDIqOJ"
+ },
+ "outputs": [],
+ "source": [
+ " train_data, val_data, test_data = split_dataset(\n",
+ " mp_dataset,\n",
+ " frac_list=[0.6, 0.2, 0.2],\n",
+ " shuffle=True,\n",
+ " random_state=42,\n",
+ ")\n",
+ "train_loader, val_loader, test_loader = MGLDataLoader(\n",
+ " train_data=train_data,\n",
+ " val_data=val_data,\n",
+ " test_data=test_data,\n",
+ " batch_size=64,\n",
+ " num_workers=0,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "id": "jREU_HYVIvoG"
+ },
+ "outputs": [],
+ "source": [
+ "# setup the embedding layer for node attributes\n",
+ "node_embed = torch.nn.Embedding(len(elem_list), 16)\n",
+ "# define the bond expansion\n",
+ "bond_expansion = BondExpansion(rbf_type=\"Gaussian\", initial=0.0, final=5.0, num_centers=100, width=0.5)\n",
+ "\n",
+ "# setup the architecture of MEGNet model\n",
+ "model = MEGNet(\n",
+ " dim_node_embedding=16,\n",
+ " dim_edge_embedding=100,\n",
+ " dim_state_embedding=2,\n",
+ " nblocks=3,\n",
+ " hidden_layer_sizes_input=(64, 32),\n",
+ " hidden_layer_sizes_conv=(64, 64, 32),\n",
+ " nlayers_set2set=1,\n",
+ " niters_set2set=2,\n",
+ " hidden_layer_sizes_output=(32, 16),\n",
+ " is_classification=False,\n",
+ " activation_type=\"softplus2\",\n",
+ " bond_expansion=bond_expansion,\n",
+ " #collate_fn=collate_fn, shivani - not needed now?\n",
+ " cutoff=4.0,\n",
+ " gauss_width=0.5,\n",
+ ")\n",
+ "\n",
+ "# setup the MEGNetTrainer\n",
+ "lit_module = ModelLightningModule(model=model)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 656,
+ "referenced_widgets": [
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+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO: GPU available: False, used: False\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n",
+ "INFO: TPU available: False, using: 0 TPU cores\n",
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+ "INFO: HPU available: False, using: 0 HPUs\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
+ "INFO: \n",
+ " | Name | Type | Params | Mode \n",
+ "----------------------------------------------------\n",
+ "0 | model | MEGNet | 189 K | train\n",
+ "1 | mae | MeanAbsoluteError | 0 | train\n",
+ "2 | rmse | MeanSquaredError | 0 | train\n",
+ "----------------------------------------------------\n",
+ "189 K Trainable params\n",
+ "100 Non-trainable params\n",
+ "189 K Total params\n",
+ "0.758 Total estimated model params size (MB)\n",
+ "109 Modules in train mode\n",
+ "0 Modules in eval mode\n",
+ "INFO:lightning.pytorch.callbacks.model_summary:\n",
+ " | Name | Type | Params | Mode \n",
+ "----------------------------------------------------\n",
+ "0 | model | MEGNet | 189 K | train\n",
+ "1 | mae | MeanAbsoluteError | 0 | train\n",
+ "2 | rmse | MeanSquaredError | 0 | train\n",
+ "----------------------------------------------------\n",
+ "189 K Trainable params\n",
+ "100 Non-trainable params\n",
+ "189 K Total params\n",
+ "0.758 Total estimated model params size (MB)\n",
+ "109 Modules in train mode\n",
+ "0 Modules in eval mode\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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},
- "execution_count": 7,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "dict_keys(['jid', 'spg_number', 'spg_symbol', 'formula', 'formation_energy_peratom', 'func', 'optb88vdw_bandgap', 'atoms', 'slme', 'magmom_oszicar', 'spillage', 'elastic_tensor', 'effective_masses_300K', 'kpoint_length_unit', 'maxdiff_mesh', 'maxdiff_bz', 'encut', 'optb88vdw_total_energy', 'epsx', 'epsy', 'epsz', 'mepsx', 'mepsy', 'mepsz', 'modes', 'magmom_outcar', 'max_efg', 'avg_elec_mass', 'avg_hole_mass', 'icsd', 'dfpt_piezo_max_eij', 'dfpt_piezo_max_dij', 'dfpt_piezo_max_dielectric', 'dfpt_piezo_max_dielectric_electronic', 'dfpt_piezo_max_dielectric_ionic', 'max_ir_mode', 'min_ir_mode', 'n-Seebeck', 'p-Seebeck', 'n-powerfact', 'p-powerfact', 'ncond', 'pcond', 'nkappa', 'pkappa', 'ehull', 'Tc_supercon', 'dimensionality', 'efg', 'xml_data_link', 'typ', 'exfoliation_energy', 'spg', 'crys', 'density', 'poisson', 'raw_files', 'nat', 'bulk_modulus_kv', 'shear_modulus_gv', 'mbj_bandgap', 'hse_gap', 'reference', 'search'])"
- ]
- },
- "metadata": {},
- "execution_count": 7
- }
+ "text/plain": [
+ "Sanity Checking: | | 0/? [00:00<?, ?it/s]"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "source": [
- " ## Let's make a dataframe from this:\n",
- "import pandas as pd\n",
- "import numpy as np"
- ],
- "metadata": {
- "id": "6HEIHJvTGPDs"
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "56705796817d43028c6eddfefc18336f",
+ "version_major": 2,
+ "version_minor": 0
},
- "execution_count": 8,
- "outputs": []
+ "text/plain": [
+ "Training: | | 0/? [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "source": [
- "df=pd.DataFrame(dft_3d)\n",
- "df.head()"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 429
- },
- "id": "RT5c9__tGRKC",
- "outputId": "ca6651ee-1d6d-4930-ff8d-aa88babf8e08"
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "73653ee65cde4122ba0268dabc34f827",
+ "version_major": 2,
+ "version_minor": 0
},
- "execution_count": 9,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- " jid spg_number spg_symbol formula formation_energy_peratom \\\n",
- "0 JVASP-90856 129 P4/nmm TiCuSiAs -0.42762 \n",
- "1 JVASP-86097 221 Pm-3m DyB6 -0.41596 \n",
- "2 JVASP-64906 119 I-4m2 Be2OsRu 0.04847 \n",
- "3 JVASP-98225 14 P2_1/c KBi -0.44140 \n",
- "4 JVASP-10 164 P-3m1 VSe2 -0.71026 \n",
- "\n",
- " func optb88vdw_bandgap \\\n",
- "0 OptB88vdW 0.000 \n",
- "1 OptB88vdW 0.000 \n",
- "2 OptB88vdW 0.000 \n",
- "3 OptB88vdW 0.472 \n",
- "4 OptB88vdW 0.000 \n",
- "\n",
- " atoms slme magmom_oszicar ... \\\n",
- "0 {'lattice_mat': [[3.566933224304235, 0.0, -0.0... na 0.0 ... \n",
- "1 {'lattice_mat': [[4.089078911208881, 0.0, 0.0]... na 0.0 ... \n",
- "2 {'lattice_mat': [[-1.833590720595598, 1.833590... na 0.0 ... \n",
- "3 {'lattice_mat': [[7.2963518353359165, 0.0, 0.0... na 0.0 ... \n",
- "4 {'lattice_mat': [[1.6777483798834445, -2.90594... na 0.0 ... \n",
- "\n",
- " density poisson raw_files nat \\\n",
- "0 5.956 na [] 8 \n",
- "1 5.522 na [OPT-LOPTICS,JVASP-86097.zip,https://ndownload... 7 \n",
- "2 10.960 na [OPT-LOPTICS,JVASP-64906.zip,https://ndownload... 4 \n",
- "3 5.145 na [] 32 \n",
- "4 5.718 0.23 [FD-ELAST,JVASP-10.zip,https://ndownloader.fig... 3 \n",
- "\n",
- " bulk_modulus_kv shear_modulus_gv mbj_bandgap hse_gap \\\n",
- "0 na na na na \n",
- "1 na na na na \n",
- "2 na na na na \n",
- "3 na na na na \n",
- "4 48.79 33.05 0.0 na \n",
- "\n",
- " reference search \n",
- "0 mp-1080455 -As-Cu-Si-Ti \n",
- "1 mp-568319 -B-Dy \n",
- "2 auid-3eaf68dd483bf4f4 -Be-Os-Ru \n",
- "3 mp-31104 -Bi-K \n",
- "4 mp-694 -Se-V \n",
- "\n",
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- " </thead>\n",
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- " <tr>\n",
- " <th>0</th>\n",
- " <td>JVASP-90856</td>\n",
- " <td>129</td>\n",
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- " <td>TiCuSiAs</td>\n",
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- " <td>0.000</td>\n",
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- " <td>7</td>\n",
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- " <tr>\n",
- " <th>2</th>\n",
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- " <td>Be2OsRu</td>\n",
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- " <td>0.000</td>\n",
- " <td>{'lattice_mat': [[-1.833590720595598, 1.833590...</td>\n",
- " <td>na</td>\n",
- " <td>0.0</td>\n",
- " <td>...</td>\n",
- " <td>10.960</td>\n",
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- " <td>4</td>\n",
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- " <td>-0.44140</td>\n",
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- " <td>0.472</td>\n",
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- " <td>-0.71026</td>\n",
- " <td>OptB88vdW</td>\n",
- " <td>0.000</td>\n",
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- " <td>na</td>\n",
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- " border-color: transparent;\n",
- " border-left-color: var(--fill-color);\n",
- " border-top-color: var(--fill-color);\n",
- " }\n",
- " 30% {\n",
- " border-color: transparent;\n",
- " border-left-color: var(--fill-color);\n",
- " border-top-color: var(--fill-color);\n",
- " border-right-color: var(--fill-color);\n",
- " }\n",
- " 40% {\n",
- " border-color: transparent;\n",
- " border-right-color: var(--fill-color);\n",
- " border-top-color: var(--fill-color);\n",
- " }\n",
- " 60% {\n",
- " border-color: transparent;\n",
- " border-right-color: var(--fill-color);\n",
- " }\n",
- " 80% {\n",
- " border-color: transparent;\n",
- " border-right-color: var(--fill-color);\n",
- " border-bottom-color: var(--fill-color);\n",
- " }\n",
- " 90% {\n",
- " border-color: transparent;\n",
- " border-bottom-color: var(--fill-color);\n",
- " }\n",
- " }\n",
- "</style>\n",
- "\n",
- " <script>\n",
- " async function quickchart(key) {\n",
- " const quickchartButtonEl =\n",
- " document.querySelector('#' + key + ' button');\n",
- " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
- " quickchartButtonEl.classList.add('colab-df-spinner');\n",
- " try {\n",
- " const charts = await google.colab.kernel.invokeFunction(\n",
- " 'suggestCharts', [key], {});\n",
- " } catch (error) {\n",
- " console.error('Error during call to suggestCharts:', error);\n",
- " }\n",
- " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
- " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
- " }\n",
- " (() => {\n",
- " let quickchartButtonEl =\n",
- " document.querySelector('#df-66c438ee-b30e-48e1-808e-7b2da6583b0f button');\n",
- " quickchartButtonEl.style.display =\n",
- " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
- " })();\n",
- " </script>\n",
- "</div>\n",
- "\n",
- " </div>\n",
- " </div>\n"
- ],
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "dataframe",
- "variable_name": "df"
- }
- },
- "metadata": {},
- "execution_count": 9
- }
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "source": [
- "## Count number of entries for each property\n",
- "for i in df.columns.values:\n",
- " val=df[i].replace('na',np.nan).dropna().values\n",
- " print(i,len(val))"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "FTsZSTDbGUbH",
- "outputId": "3a024410-29ed-442a-c405-a90b5985da09"
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "d79c6b456ffb4289921288199c1a5e93",
+ "version_major": 2,
+ "version_minor": 0
},
- "execution_count": 10,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "jid 75993\n",
- "spg_number 75993\n",
- "spg_symbol 75993\n",
- "formula 75993\n",
- "formation_energy_peratom 75993\n",
- "func 75993\n",
- "optb88vdw_bandgap 75993\n",
- "atoms 75993\n",
- "slme 9770\n",
- "magmom_oszicar 71320\n",
- "spillage 11377\n",
- "elastic_tensor 25513\n",
- "effective_masses_300K 75993\n",
- "kpoint_length_unit 75671\n",
- "maxdiff_mesh 5861\n",
- "maxdiff_bz 5861\n",
- "encut 75670\n",
- "optb88vdw_total_energy 75993\n",
- "epsx 52168\n",
- "epsy 52168\n",
- "epsz 52168\n",
- "mepsx 18293\n",
- "mepsy 18293\n",
- "mepsz 18293\n",
- "modes 13910\n",
- "magmom_outcar 74261\n",
- "max_efg 11871\n",
- "avg_elec_mass 17645\n",
- "avg_hole_mass 17645\n",
- "icsd 75993\n",
- "dfpt_piezo_max_eij 4799\n",
- "dfpt_piezo_max_dij 3347\n",
- "dfpt_piezo_max_dielectric 4706\n",
- "dfpt_piezo_max_dielectric_electronic 4809\n",
- "dfpt_piezo_max_dielectric_ionic 4809\n",
- "max_ir_mode 4805\n",
- "min_ir_mode 4809\n",
- "n-Seebeck 23218\n",
- "p-Seebeck 23218\n",
- "n-powerfact 23218\n",
- "p-powerfact 23218\n",
- "ncond 23218\n",
- "pcond 23218\n",
- "nkappa 23218\n",
- "pkappa 23218\n",
- "ehull 75993\n",
- "Tc_supercon 1058\n",
- "dimensionality 75560\n",
- "efg 75993\n",
- "xml_data_link 75993\n",
- "typ 75993\n",
- "exfoliation_energy 813\n",
- "spg 75993\n",
- "crys 75993\n",
- "density 75993\n",
- "poisson 23597\n",
- "raw_files 75993\n",
- "nat 75993\n",
- "bulk_modulus_kv 23824\n",
- "shear_modulus_gv 23824\n",
- "mbj_bandgap 19805\n",
- "hse_gap 56\n",
- "reference 75993\n",
- "search 75993\n"
- ]
- }
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "source": [
- "from jarvis.core.atoms import Atoms\n",
- "bm=df[df.bulk_modulus_kv != 'na']\n",
- "data = [(Atoms.from_dict(bm.iloc[i]['atoms']).pymatgen_converter(), bm.iloc[i].bulk_modulus_kv) for i in range(len(bm))]"
- ],
- "metadata": {
- "id": "rW4KEnICGVxE"
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "3b8db07a150a4f058a540e5d584a981c",
+ "version_major": 2,
+ "version_minor": 0
},
- "execution_count": 11,
- "outputs": []
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "source": [
- "import itertools\n",
- "def get_stoichiometry(elements):\n",
- " return [(g[0], len(list(g[1]))) for g in itertools.groupby(elements)]"
- ],
- "metadata": {
- "id": "0gQUS5rQGaK5"
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8f22afc4e41a45f28fb1c81f37c554fc",
+ "version_major": 2,
+ "version_minor": 0
},
- "execution_count": 12,
- "outputs": []
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "source": [
- " ## Use all the material dataset for training the bulk modulus\n",
- "from tqdm import tqdm\n",
- "\n",
- "stoichs=[] #stoichiometry\n",
- "bulk=[] #only include positive bulk modulus\n",
- "\n",
- "for i in tqdm(range(len(bm))):\n",
- " if (bm.iloc[i]['bulk_modulus_kv'])>1:\n",
- " stoichs.append(Atoms.from_dict(bm.iloc[i]['atoms']).pymatgen_converter())\n",
- " bulk.append(bm.iloc[i]['bulk_modulus_kv'])\n"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "YaMVVrguGe2s",
- "outputId": "25dcd209-e980-4a20-c650-eb17046dda62"
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "21db824539da4758a8aa5c6351949cc5",
+ "version_major": 2,
+ "version_minor": 0
},
- "execution_count": 13,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "100%|██████████| 23824/23824 [00:36<00:00, 656.64it/s] \n"
- ]
- }
+ "text/plain": [
+ "Validation: | | 0/? [00:00<?, ?it/s]"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "source": [
- "data_ran=list(zip(stoichs,bulk))\n",
- "#write out the dataset, to train later\n",
- "import pickle\n",
- "with open('data_ran.pickle', 'wb') as f:\n",
- " pickle.dump(data_ran, f)"
- ],
- "metadata": {
- "id": "0lxmfaHEHhs6"
- },
- "execution_count": 18,
- "outputs": []
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO: `Trainer.fit` stopped: `max_epochs=5` reached.\n",
+ "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=5` reached.\n"
+ ]
+ }
+ ],
+ "source": [
+ "logger = CSVLogger(\"logged\", name=\"MEGNet_training\")\n",
+ "trainer = Trainer(max_epochs=5, accelerator=\"cpu\", logger=logger) #set to SMALL NUMBER FOR TESTING, PLEASE CHANGE.\n",
+ "trainer.fit(model=lit_module, train_dataloaders=train_loader, val_dataloaders=val_loader)\n",
+ "\n",
+ "warnings.simplefilter(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 430
},
+ "id": "JpFwUt4_JMjZ",
+ "outputId": "06c0bfc7-7c4b-40fa-b4d6-35e054a407bc"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- " #read in the dataset\n",
- "data_ran=pd.read_pickle('./data_ran.pickle')"
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "metrics = pd.read_csv(\"logged/MEGNet_training/version_0/metrics.csv\")\n",
+ "metrics[\"train_MAE\"].dropna().plot()\n",
+ "metrics[\"val_MAE\"].dropna().plot()\n",
+ "\n",
+ "_ = plt.legend()\n",
+ "#plt.savefig(\"loss.jpg\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.7"
+ },
+ "widgets": {
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+ "model_module_version": "1.2.0",
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+ "state": {
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- "Full Formula (Al2 O1)\n",
- "Reduced Formula: Al2O\n",
- "abc : 2.691974 2.821216 5.955222\n",
- "angles: 90.000000 90.000000 90.000000\n",
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- "\n",
- "# setup the MEGNetTrainer\n",
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+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "f7321d7b1b9a4dbe905092f388240f23": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_79500fbf2f93417796e265786cb54a43",
+ "IPY_MODEL_b755785fab01467cb5989d11385e716c",
+ "IPY_MODEL_c26f6b42e3f348f2a4e161765d99042e"
],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 430
- },
- "id": "JpFwUt4_JMjZ",
- "outputId": "06c0bfc7-7c4b-40fa-b4d6-35e054a407bc"
- },
- "execution_count": 25,
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ],
- "image/png": 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JWVlZWLJkCQ4cOICkpCSkp6ffcCbxhQsXMH/+fNx2222tXlu+fDnefPNNrFmzBnv27IGvry/S09PR0NDgaHnUTYmRGgwI9EZdoxE788ulLoeIPIwbrAOh6zjrZ+pwYFmxYgUeffRRZGZmYvjw4VizZg18fHzafWQ1ABiNRsyaNQt/+MMfMHjwYLvXRFHEypUr8fvf/x733XcfEhMTsWHDBly+fBn/+te/HP5C1D2CIGDySPOzhbYd5bOFiKh3yOVyAOj2DrDkeqw/U+vPuKscmsPS2NiI/fv3Y+HChbZzMpkMaWlpyMvLa/d9L7/8MkJDQ/HII4/g22+/tXvt/PnzKCkpQVpamu2cRqNBamoq8vLyMGPGjFb3MxgMMBgMtr/r9XpHvgbdQMbIcLz77XnknjC3hdRe3fuXjIjoRhQKBXx8fFBeXg4vLy+X2ayMusdkMqG8vBw+Pj52jwfoCofeXVFRAaPRCK1Wa3deq9Xi5MmTbb5n9+7dWLt2LQ4ePNjm69ad9dq6Z1u77gFAdnY2/vCHPzhSOjng5qhARGjUuKxrwLenK3D3cO2N30RE1A2CICA8PBznz5/HxYsXpS6HnEgmk2HgwIEQBKFb9+nRVULV1dX4xS9+gXfffRchISFOu+/ChQuRlZVl+7ter0dUVJTT7u/pBEHApIRwrPvuPLYeKWZgIaJeoVQqERcXx7aQm1EqlU4ZMXMosISEhEAul6O0tNTufGlpKcLCwlpdf/bsWVy4cAFTp061nbM+Q0GhUCA/P9/2vtLSUoSHX1tGW1paiuTk5DbrUKlUUKlUjpRODpqSGIZ1353HjuOlMDQboVKwLUREPU8mk3GnW2qTQ5FHqVRi1KhRyM3NtZ0zmUzIzc3F2LFjW10fHx+PI0eO4ODBg7bj3nvvxR133IGDBw8iKioKMTExCAsLs7unXq/Hnj172rwn9Y6bo4IQFqBGtaEZu09XSF0OERF5OIdbQllZWZgzZw5SUlIwZswYrFy5ErW1tcjMzAQAPPzwwxgwYACys7OhVquRkJBg9/7AwEAAsDv/7LPP4pVXXkFcXBxiYmKwaNEiREREtNqvhXqPTCZgUkIY1n9/AVuPlOCuYWwLERGRdBwOLNOnT0d5eTkWL16MkpISJCcnIycnxzZptqCgwOFe1QsvvIDa2lo89thjqKqqwq233oqcnBwOC0ps8shwrP/+ArYfL0Fj80goFZy1T0RE0uDTmqldJpOIn2TnoqzagPcyR+OOoaFSl0RERG6ET2smp7C2hQBg62FuIkdERNJhYKEOWZ8t9OXxUjQZTRJXQ0REnoqBhTo0OjoYIX5K6Oqb8P3ZSqnLISIiD8XAQh2SywSkj7A8W+gI20JERCQNBha6oSmWttAXx0rYFiIiIkkwsNANjYkJRrCvElfrmrDn3BWpyyEiIg/EwEI3pJDLbG2hLWwLERGRBBhYqFMmjzQHli+PlaCZbSEiIuplDCzUKT8Z3A9BPl6orG3E3gtsCxERUe9iYKFO8ZLLMHG4ZRM5toWIiKiXMbBQp01ONK8WyjlaCqOpzz/RgYiI+hAGFuq0cUP6QePthYoaA/axLURERL2IgYU6zdwWMj+Vm5vIERFRb2JgIYdYny207WgJTGwLERFRL2FgIYeMjw2Bv1qBsmoD9hdclbocIiLyEAws5BClQoa7LW2hLYfZFiIiot7BwEIOsz5bKIdtISIi6iUMLOSwW+NC4K9SoETfgB8L2RYiIqKex8BCDlMp5EiztIW2HimRuBoiIvIEDCzUJRkJ5l1vtx0pZluIiIh6HAMLdcntN/WHr1KOy7oGHCqqkrocIiJycwws1CVqLznuGmZtC3G1EBER9SwGFuoy6yZyW4+UQBTZFiIiop7DwEJdNmFof/go5bhUVY/DRTqpyyEiIjfGwEJdpvaS4474UADA1qNsCxERUc9hYKFumWJrCxWzLURERD2GgYW6ZcLQ/lB7yVB4pR7HLuulLoeIiNwUAwt1i49SgTstbaEtXC1EREQ9hIGFui0jwdwW2sa2EBER9RAGFuq2O+NDoVLIcKGyDseL2RYiIiLnY2ChbvNVKTBhaH8AwDY+W4iIiHoAAws5xWSuFiIioh7EwEJOcdcwLZQKGc5V1CK/tFrqcoiIyM0wsJBT+KkU+OlN5rbQVraFiIjIyRhYyGkmjwwDwIchEhGR8zGwkNPcNUwLpVyGM2U1OM22EBEROREDCzlNgNoLt8WFAOAmckRE5FwMLORU1tVCXN5MRETOxMBCTpU2XAsvuYD80mqcKauRuhwiInITDCzkVBpvL9waa24LbWNbiIiInISBhZwuw9IW4jwWIiJyFgYWcrqJw7VQyAScLKnGuXK2hYiIqPsYWMjpAn2UGGdtCx3l5FsiIuo+BhbqEVO4iRwRETkRAwv1iLuHh0EuE3Dssh4XK2ulLoeIiPo4BhbqEcG+Sowb0g8Any1ERETdx8BCPSYjwbxaiG0hIiLqLgYW6jETR2ghE4Ajl3QovFIndTlERNSHMbBQjwnxU+Eng61tIY6yEBFR1zGwUI+ybiK3lcubiYioGxhYqEdNGhEGQQAOFVah6CrbQkRE1DUMLNSj+vurMCY6GACQw1EWIiLqIgYW6nFTEvlsISIi6h4GFupx1rbQjwVVuFxVL3U5RETUB3UpsKxatQrR0dFQq9VITU3F3r172732s88+Q0pKCgIDA+Hr64vk5GR88MEHdtfMnTsXgiDYHZMmTepKaeSCQgPUGD2IbSEiIuo6hwPLpk2bkJWVhSVLluDAgQNISkpCeno6ysrK2rw+ODgYL774IvLy8nD48GFkZmYiMzMTX3zxhd11kyZNQnFxse34+OOPu/aNyCVl8NlCRETUDQ4HlhUrVuDRRx9FZmYmhg8fjjVr1sDHxwfr1q1r8/oJEybggQcewLBhwzBkyBA888wzSExMxO7du+2uU6lUCAsLsx1BQUFd+0bkkqy73v734lWU6BokroaIiPoahwJLY2Mj9u/fj7S0tGs3kMmQlpaGvLy8G75fFEXk5uYiPz8ft99+u91rO3fuRGhoKIYOHYonnngClZWV7d7HYDBAr9fbHeTawjRqjBpkDqE5RznKQkREjnEosFRUVMBoNEKr1dqd12q1KClpf26CTqeDn58flEolpkyZgrfeegt333237fVJkyZhw4YNyM3Nxeuvv45du3YhIyMDRqOxzftlZ2dDo9HYjqioKEe+BklkMjeRIyKiLlL0xof4+/vj4MGDqKmpQW5uLrKysjB48GBMmDABADBjxgzbtSNHjkRiYiKGDBmCnTt34q677mp1v4ULFyIrK8v2d71ez9DSB2QkhGHp58ex78IVlOkbEBqglrokIiLqIxwaYQkJCYFcLkdpaand+dLSUoSFhbX/ITIZYmNjkZycjOeeew7Tpk1DdnZ2u9cPHjwYISEhOHPmTJuvq1QqBAQE2B3k+iICvXHzwECIIvDFMY6yEBFR5zkUWJRKJUaNGoXc3FzbOZPJhNzcXIwdO7bT9zGZTDAYDO2+XlRUhMrKSoSHhztSHvUBkxO4iRwRETnO4VVCWVlZePfdd/H+++/jxIkTeOKJJ1BbW4vMzEwAwMMPP4yFCxfars/Ozsb27dtx7tw5nDhxAn/605/wwQcfYPbs2QCAmpoaPP/88/jhhx9w4cIF5Obm4r777kNsbCzS09Od9DXJVViXN+89fwXl1e2HViIiopYcnsMyffp0lJeXY/HixSgpKUFycjJycnJsE3ELCgogk13LQbW1tXjyySdRVFQEb29vxMfH48MPP8T06dMBAHK5HIcPH8b777+PqqoqREREYOLEiVi6dClUKpWTvia5isggHyRFanCoSIcvjpVg9k8GSV0SERH1AYIoiqLURXSXXq+HRqOBTqfjfJY+4G+7ziJ720mMj+2Hjb/8idTlEBGRRBz5/c1nCVGvsy5vzjtbicoatoWIiOjGGFio10UF+2DkAA1MIvDl8dIbv4GIiDweAwtJgs8WIiIiRzCwkCSsy5u/P1uJq7WNEldDRESujoGFJBEd4ovh4QEwmkR8eZybyBERUccYWEgyk21tIQYWIiLqGAMLSca6Wui7MxWoqmNbiIiI2sfAQpIZ3N8P8WH+aDaJ2M7VQkRE1AEGFpKUdZSFq4WIiKgjDCwkKes8lt1nKqCrb5K4GiIiclUMLCSp2FB/3KT1Q5NRxA62hYiIqB0MLCS5DMueLNuOsi1ERERtY2AhyU1JNAeWb05VoLqBbSEiImqNgYUkFxfqhyH9fdFoNCH3RJnU5RARkQtiYCHJCYKAKVwtREREHWBgIZeQYQksO0+Vo8bQLHE1RETkahhYyCXEh/ljcIgvGptN+Ook20JERGSPgYVcgiAIyLA+W+gw20JERGSPgYVchnXX26/zy1DLthAREbXAwEIuY3h4AAb184Gh2YSv89kWIiKiaxhYyGUIgmAbZdl2pETiaoiIyJUwsJBLmWzZ9fark2WobzRKXA0REbkKBhZyKQkDAhAV7I36JiN2si1EREQWDCzkUgRBsI2ybOEmckREZMHAQi7HOo/lq5NlaGhiW4iIiBhYyAUlRmowINAbdY1G7Mwvl7ocIiJyAQws5HIEQUBGgnkTuW1H2RYiIiIGFnJRkxPNbaHcE2wLERERAwu5qOTIQIRr1KgxNOPb0xVSl0NERBJjYCGXJJMJyLCsFtrK1UJERB6PgYVc1mTLwxB3HC+FoZltISIiT8bAQi7rloFB0AaoUG1oxm62hYiIPBoDC7ks+7YQny1EROTJGFjIpVk3kdt+vASNzSaJqyEiIqkwsJBLGzUoCP39VdA3NOO7s2wLERF5KgYWcmlyWYtN5LhaiIjIYzGwkMuzzmP58ngpmoxsCxEReSIGFnJ5Y2KCEeKnRFVdE/LOVkpdDhERSYCBhVyeXCYgfYS5LcRN5IiIPBMDC/UJUyyrhb44VoJmtoWIiDwOAwv1CWNighHsq8TVuib8cO6K1OUQEVEvY2ChPkEhl11rCx1lW4iIyNMwsFCfYX220BdH2RYiIvI0DCzUZ/xkcD8E+XihsrYRey+wLURE5EkYWKjP8JLLMHE4VwsREXkiBhbqUyYnmlcL5RwthdEkSlwNERH1FgYW6lPGDekHjbcXKmoM2Me2EBGRx2BgoT7F3BbSAuCzhYiIPAkDC/U5ky2byG07WgIT20JERB6BgYX6nPGxIfBXK1BWbcD+gqtSl0NERL2AgYX6HKVChrstbaEth9kWIiLyBAws1CdNTrCuFmJbiIjIEzCwUJ90200h8FMpUKJvwI+FbAsREbm7LgWWVatWITo6Gmq1Gqmpqdi7d2+713722WdISUlBYGAgfH19kZycjA8++MDuGlEUsXjxYoSHh8Pb2xtpaWk4ffp0V0ojD6FSyJE2LBQAsPVIicTVEBFRT3M4sGzatAlZWVlYsmQJDhw4gKSkJKSnp6OsrKzN64ODg/Hiiy8iLy8Phw8fRmZmJjIzM/HFF1/Yrlm+fDnefPNNrFmzBnv27IGvry/S09PR0NDQ9W9Gbs+2WuhIMdtCRERuThBF0aH/pU9NTcXo0aPx9ttvAwBMJhOioqLw9NNPY8GCBZ26xy233IIpU6Zg6dKlEEUREREReO655zB//nwAgE6ng1arxfr16zFjxowb3k+v10Oj0UCn0yEgIMCRr0N9WEOTEaOWbkdtoxGbnxyHmwcGSV0SERE5wJHf3w6NsDQ2NmL//v1IS0u7dgOZDGlpacjLy7vh+0VRRG5uLvLz83H77bcDAM6fP4+SkhK7e2o0GqSmpnbqnuS51F5y3DXMvFqIzxYiInJvDgWWiooKGI1GaLVau/NarRYlJe3PI9DpdPDz84NSqcSUKVPw1ltv4e677wYA2/scuafBYIBer7c7yDNNHml9GGIJHBwsJCKiPqRXVgn5+/vj4MGD2LdvH5YtW4asrCzs3Lmzy/fLzs6GRqOxHVFRUc4rlvqUCUND4aOU41JVPY5c0kldDhER9RCHAktISAjkcjlKS0vtzpeWliIsLKz9D5HJEBsbi+TkZDz33HOYNm0asrOzAcD2PkfuuXDhQuh0OttRWFjoyNcgN6L2kuOOePNqoS1sCxERuS2HAotSqcSoUaOQm5trO2cymZCbm4uxY8d2+j4mkwkGgwEAEBMTg7CwMLt76vV67Nmzp917qlQqBAQE2B3kuabYVguxLURE5K4Ujr4hKysLc+bMQUpKCsaMGYOVK1eitrYWmZmZAICHH34YAwYMsI2gZGdnIyUlBUOGDIHBYMDWrVvxwQcfYPXq1QAAQRDw7LPP4pVXXkFcXBxiYmKwaNEiRERE4P7773feNyW3NWFof6i9ZCi4Uodjl/VIGKCRuiQiInIyhwPL9OnTUV5ejsWLF6OkpATJycnIycmxTZotKCiATHZt4Ka2thZPPvkkioqK4O3tjfj4eHz44YeYPn267ZoXXngBtbW1eOyxx1BVVYVbb70VOTk5UKvVTviK5O58lArcGR+KrUdKsPVIMQMLEZEbcngfFlfEfVjoP4cu4+mPf0R0Px98PX8CBEGQuiQiIrqBHtuHhchV3RkfCpVChguVdThRXC11OURE5GQMLOQWfFUKTBjaHwA3kSMickcMLOQ2rM8W2nqkmKuFiIjcDAMLuY27hmmhVMhwrqIW+aVsCxERuRMGFnIbfioFfnqTtS3U/qMiiIio72FgIbdy7dlCnMdCROROGFjIrdw1TAulXIYzZTU4zbYQEZHbYGAhtxKg9sJtcSEA+GwhIiJ3wsBCbiejxbOFiIjIPTCwkNu5e5gWXnIB+aXVOFNWI3U5RETkBAws5HY0Pl4YH2tuC21jW4iIyC0wsJBbsm4ix3ksRETugYGF3NLE4VooZAJOllTjXDnbQkREfR0DC7mlQB8lxlnbQkc5+ZaIqK9jYCG3NTmBm8gREbkLBhZyWxNHhEEuE3Dssh4XK2ulLoeIiLqBgYXcVrCvEmMH9wPAZwsREfV1DCzk1qyrhbYdZVuIiKgvY2AhtzZxhBYyAThcpEPhlTqpyyEioi5iYCG3FuKnwk8sbSGOshAR9V0MLOT2MmybyHEeCxFRX8XAQm5v0ogwCAJwqLAKRVfZFiIi6osYWMjt9fdXYUx0MAAgh5vIERH1SQws5BGmJJrbQtxEjoiob2JgIY9gbQsdKKjC5ap6qcshIiIHMbCQRwgNUGP0ILaFiIj6KgYW8hgZI/lsISKivoqBhTxGRoJ5Hst/L15Fia5B4mqIiMgRDCzkMcI0aowaFAQAyOEmckREfQoDC3mUjARLW4jzWIiI+hQGFvIo1l1v9124gjI920JERH0FAwt5lAGB3kiOCoQoAl8c4ygLEVFfwcBCHmeK7dlCnMdCRNRXMLCQx5lkmcey9/wVlFcbJK6GiIg6g4GFPE5UsA+SIjUwsS1ERNRnMLCQR7JOvt3G5c1ERH0CAwt5pMmWTeTyzlaisoZtISIiV8fAQh5pYD8fJAwIgEkEvjxeKnU5RER0Awws5LEmW9pCfLYQEZHrY2Ahj2VtC31/thJXaxslroaIiDrCwEIeKzrEF8PDA2A0idjOthARkUtjYCGPNnmkeU8WbiJHROTaGFjIo1nnsXx3pgK6uiaJqyEiovYwsJBHG9zfD/Fh/mg2ifjyODeRIyJyVQws5PEm2zaRY2AhInJVDCzk8azzWL49XQ5dPdtCRESuiIGFPF5sqD9u0vqhySgi9wRXCxERuSIGFiIAGQncRI6IyJUxsBABmJJoDizfnKpAdQPbQkREroaBhQhAXKgfhvT3RaPRhNwTZVKXQ0RE12FgIQIgCAKm8NlCREQui4GFyCLDElh2nipHjaFZ4mqIiKglBhYii/gwf8SE+KKx2YSvTrItRETkShhYiCwEQbDtybL1MNtCRESupEuBZdWqVYiOjoZarUZqair27t3b7rXvvvsubrvtNgQFBSEoKAhpaWmtrp87dy4EQbA7Jk2a1JXSiLrFurz56/wy1LItRETkMhwOLJs2bUJWVhaWLFmCAwcOICkpCenp6Sgra3sIfefOnZg5cya+/vpr5OXlISoqChMnTsSlS5fsrps0aRKKi4ttx8cff9y1b0TUDSMiAjConw8MzSZ8nc+2EBGRq3A4sKxYsQKPPvooMjMzMXz4cKxZswY+Pj5Yt25dm9dv3LgRTz75JJKTkxEfH4+///3vMJlMyM3NtbtOpVIhLCzMdgQFBXXtGxF1gyAItlGWbUf4bCEiIlfhUGBpbGzE/v37kZaWdu0GMhnS0tKQl5fXqXvU1dWhqakJwcHBdud37tyJ0NBQDB06FE888QQqKyvbvYfBYIBer7c7iJzFurz5q5NlqG80SlwNEREBDgaWiooKGI1GaLVau/NarRYlJZ37f6O//e1vERERYRd6Jk2ahA0bNiA3Nxevv/46du3ahYyMDBiNbf+yyM7OhkajsR1RUVGOfA2iDiUMCEBkkDfqm4zYybYQEZFL6NVVQq+99ho++eQTbN68GWq12nZ+xowZuPfeezFy5Ejcf//9+Pzzz7Fv3z7s3LmzzfssXLgQOp3OdhQWFvbSNyBP0HITuS3cRI6IyCU4FFhCQkIgl8tRWmr/RNvS0lKEhYV1+N433ngDr732Gr788kskJiZ2eO3gwYMREhKCM2fOtPm6SqVCQECA3UHkTBkt2kINTWwLERFJzaHAolQqMWrUKLsJs9YJtGPHjm33fcuXL8fSpUuRk5ODlJSUG35OUVERKisrER4e7kh5RE6TFKnBgEBv1DUasTO/XOpyiIg8nsMtoaysLLz77rt4//33ceLECTzxxBOora1FZmYmAODhhx/GwoULbde//vrrWLRoEdatW4fo6GiUlJSgpKQENTU1AICamho8//zz+OGHH3DhwgXk5ubivvvuQ2xsLNLT0530NYkcY14tZB413HaUbSEiIqkpHH3D9OnTUV5ejsWLF6OkpATJycnIycmxTcQtKCiATHYtB61evRqNjY2YNm2a3X2WLFmCl156CXK5HIcPH8b777+PqqoqREREYOLEiVi6dClUKlU3vx5R101ODMffd59H7glzW0jtJZe6JCIijyWIoihKXUR36fV6aDQa6HQ6zmchpzGZRIx//SsU6xrw7sMpuHu49sZvIiKiTnPk9zefJUTUDpms5SZybAsREUmJgYWoA9aHIW4/XgpDM1cLERFJhYGFqAO3DAyCNkCFakMzvjtTIXU5REQei4GFqAMt20JbDvPZQkREUmFgIbqByZZN5LYfL0Fjs0niaoiIPBMDC9ENjBoUhP7+KugbmvHdWbaFiIikwMBCdANyWYtN5LhaiIhIEgwsRJ1gncfy5fFSNBnZFiIi6m0MLESdMCYmGCF+SlTVNSHvbKXU5RAReRwGFqJOkMsEpI8wt4W2si1ERNTrGFiIOsm6WuiLYyVoZluIiKhXMbAQdVJqTDCCfZW4WteEH85dkbocIiKPwsBC1EkKuQzpI8wPQNx6lG0hIqLexMBC5ABbW+go20JERL2JgYXIAT8Z3A+BPl6orG3E3gtsCxER9RYGFiIHeMllSB/O1UJERL2NgYXIQRkjzYEl52gpjCZR4mqIiDwDAwuRg8bHhkDj7YWKGgP2sS1ERNQrGFiIHOQll+Hu4ebVQny2EBFR72BgIeqCKZbVQtuOlsDEthARUY9jYCHqgvGxIfBXK1BWbcD+gqtSl0NE5PYYWIi6QKm41hbiaiEiop7HwELURZMTLG2hI2wLERH1NAYWoi667aYQ+KkUKNE34MfCKqnLISJyawwsRF2kUsiRNiwUANtCREQ9jYGFqBuszxbadqQYosi2EBFRT2FgIeqG22/qD1+lHJd1DTjIthARUY9hYCHqBrWXHHcNs2wid7RE4mqIiNwXAwtRN022PFtoy2G2hYiIegoDC1E3TRgaCh+lHJeq6nHkkk7qcoiI3BIDC1E3qb3kuCPevFpoC1cLERH1CAYWIiewPVvoSAnbQkREPYCBhcgJJgztD7WXDAVX6nDssl7qcoiI3A4DC5ET+CgVuGMoN5EjIuopDCxETmLdRG4rN5EjInI6BhYiJ7kzPhQqhQwXKutworha6nKIiNwKAwuRk/iqFJgwtD8AtoWIiJyNgaUjoghsfgL4cSNg4P9jphtjW4iIqGcwsHTk0n7g0EfA/z0J/DEO+PQR4PR2wNgsdWXkou6MD4VSIcO5ilrklzLkEhE5CwNLRzRRwJ2/B/rFAc31wNFPgY3TgBXDgJzfAcWHzKMwRBb+ai/cHmdtC/HZQkREzsLA0hF/LXD788Cv9wGPfgWMeRzw6QfUlgE/rAL+djvw17HA7j8DuktSV0suYkqi+dlCnMdCROQ8gugGjXa9Xg+NRgOdToeAgICe/TBjE3BmB3DoEyB/G2A0WF4QgJjbgaQZwLCpgMq/Z+sgl6VvaELK0h1oNJqw/X9uR5yW/y4QEbXFkd/fHGFxlNwLGJoB/Px9YP4pYOqbwKDxAETg/C7gX0+Y57v885fA6R2c7+KBAtReuC0uBACfLURE5CwMLN3hHQiMmgNkbgWeOQzc8XugX6x5vsuR/wU2Pgj8eTjwxYtA8WHOd/EgGS2eLURERN3HlpCziSJw6QBw+BPg6D+Busprr4UOBxKnA4k/BwIipKuRepyurgkpy7ajyShiR9ZPERvqJ3VJREQuhy0hKQkCEDkKmPxH4Ll8YOYnwPD7ALkKKDsO7FgCrBgObLgPOPgxYKiRumLqARofL4yPNbeFtrEtRETUbQwsPck232WDZb7LX4CB4wCIwLmdwL9+BbwRB/zzUfNEXpNR6orJiWybyB1lW4iIqLvYEpLC1QvA4X+YVxpdOXvtvJ8WGPmQeaVR2EjJyiPnqKprRMorO9BsEvH1/AmICfGVuiQiIpfClpCrC4oGfvoC8PR+4Je5wOhHAe9goKYUyHsbWHMr8NdxwHd/AfSXpa6WuijQR4lxlrYQ92QhIuoeBhYpCQIQmQJMecM832XGR8CwewG5Eig7BmxfzPkufdzkBG4iR0TkDGwJuaL6q8CxfwGHNwEFedfOe/mYN6VLnA4MngDI5FJVSJ10pbYRo5ftgNEkYtfzEzCoH9tCRERWbAn1dd5BQEomMC8H+M1BYMLvgODBQFOdOcR8+DPzyMsXLwIlR6WuljoQ7KvE2MH9APDZQkRE3cHA4uqCY4AJvwWePgA8sgMY/UtzoKkpscx3GQ+sHg989yagZ9vBFVlXC207yp8PEVFXsSXUFzU3Ame2m1cZncoBjI3m84IMiPmpeZVR/D2AipuVuYKKGgPGLNsBkwh8+8IdiAr2kbokIiKX0OMtoVWrViE6OhpqtRqpqanYu3dvu9e+++67uO222xAUFISgoCCkpaW1ul4URSxevBjh4eHw9vZGWloaTp8+3ZXSPINCCcRPAaZ/YN7f5Z4/A1E/AUQTcO5rYPPjwBs3AZ89Dpz9ivu7SCzET4XUGHNbiKMsRERd43Bg2bRpE7KysrBkyRIcOHAASUlJSE9PR1lZWZvX79y5EzNnzsTXX3+NvLw8REVFYeLEibh06ZLtmuXLl+PNN9/EmjVrsGfPHvj6+iI9PR0NDQ1d/2aewjsISJkHPPLFdfNdas2PB/jgAeDPI4Avf8/5LhKanGhuC23hPBYioi5xuCWUmpqK0aNH4+233wYAmEwmREVF4emnn8aCBQtu+H6j0YigoCC8/fbbePjhhyGKIiIiIvDcc89h/vz5AACdTgetVov169djxowZN7ynx7WEbkQUgaJ95pbRsc/Mq46stCOBpOnmDer8w6Sr0cOUVTcg9dVciCKw+7d3IDKIbSEioh5rCTU2NmL//v1IS0u7dgOZDGlpacjLy+vgndfU1dWhqakJwcHBAIDz58+jpKTE7p4ajQapqant3tNgMECv19sd1IIgAFFjgHtWAM+dAqZvNC+HlnkBpUfMoy0rhplHXw5tAhprpa7Y7YX6qzEm2vzvfA636icicphDgaWiogJGoxFardbuvFarRUlJ5/5H+Le//S0iIiJsAcX6PkfumZ2dDY1GYzuioqIc+RqeRaEEht0DTP/QPN9lygogKtU83+XsV8Dmx4A/xgGbfwWc/ZrzXXqQ7dlC3ESOiMhhvbqs+bXXXsMnn3yCzZs3Q61Wd/k+CxcuhE6nsx2FhYVOrNKN+QQDox8BHvkS+M2PwISFQFCMeb7LoY+BD+63zHdZBJQek7patzMpIQyCABwoqMLlqnqpyyEi6lMcCiwhISGQy+UoLS21O19aWoqwsI7nQ7zxxht47bXX8OWXXyIxMdF23vo+R+6pUqkQEBBgd5CDggcDExaYg8sj280Td9WBQHUx8P2bwOpxwOpbge/fAqrZwnAGbYAaKYOCALAtRETkKIcCi1KpxKhRo5Cbm2s7ZzKZkJubi7Fjx7b7vuXLl2Pp0qXIyclBSkqK3WsxMTEICwuzu6der8eePXs6vCc5iW2+y5/NLaPpH5r3cGk13+Vn5idMc75Lt7AtRETUNQpH35CVlYU5c+YgJSUFY8aMwcqVK1FbW4vMzEwAwMMPP4wBAwYgOzsbAPD6669j8eLF+OijjxAdHW2bl+Ln5wc/Pz8IgoBnn30Wr7zyCuLi4hATE4NFixYhIiIC999/v/O+Kd2YQmWenDtsKlB3xbzC6NAmoGgvcDbXfCj9zK8nzQCib+PzjBw0KSEMf/jPcfz34lWU6BoQpul6a5SIyJM4HFimT5+O8vJyLF68GCUlJUhOTkZOTo5t0mxBQQFksmsDN6tXr0ZjYyOmTZtmd58lS5bgpZdeAgC88MILqK2txWOPPYaqqirceuutyMnJ6dY8F+omn2DzYwBG/xKoPGseXTn8CXD1gnm+y6GPAf8IIPEhIHEGoB0udcV9QrjGG6MGBWH/xavIOVqMueNjpC6JiKhP4Nb81HmiCBTusezvshloqLr2WthIc3AZ+RDgr233FgT8/dtzeGXLCYyJCcY/Hmfbk4g8lyO/vxlYqGuaDcCpL8xPjz71BWBqMp8XZMCQO83hJX4KoOQGade7VFWP8a99BUEAMhLCkBQZiKSoQCQM0MBP5fCgJxFRn8XAQr3LNt/lE/MOu1ZKP2DYveaddTnfxc4v1u7Bt6cr7M4JAhAX6oekyEAkRgUiOTIQQ8P8oVTwoepE5J4YWEg6lWfNoy6HPgGqLl477x8BJP7cPFk3dJh09bkIQ7MR+y9exeEiHQ4VVuFwkQ6X2tibRamQYXh4AJKjApEYqUFSVCBi+vlCJhMkqJqIyLkYWEh6dvNdPgMadNdeC0s0B5eEaZzv0kJZdQMOF+pwuKgKBy1BRlff1Oo6f7XCHF4iA5EYGYjkqECuNiKiPomBhVxLUwNw+gvzEunTX7aY7yI3z3dJmgEMncz5LtcRRREFV+pwsLAKhyxB5uhlHRqaTK2u1QaobOElKTIQIyM10Hh7SVA1EVHnMbCQ66q7Ahz9p7ltZDffxR8Yfi+QaJ3vwnkbbWk2mnCqtAaHiqpwqLAKh4p0OFVaDaOp9X+NB4f4IqlFK2l4eADUXpxHRESug4GF+oaKM+bgcniT/XyXgAHm+S6JM4DQeOnq6yPqGptx7LLeFmAOF1XhYmVdq+sUMgHx4f7mVUmWlUmxoX6Qcz4MEUmEgYX6FlEECn4wb0x3dDNgaDHfJTzJsr/LNMAvVLoa+5irtY04VFRlm9R7qKgKFTWNra7zUcqRMEBzbVJvZCAig7whCAwxRNTzGFio72pqAE7lmEddTn8JmJrN51vOd7kpHVD5S1tnHyOKIi7rGszhxRJgjhTpUNtobHVtP1+lrY2UZJkTE+yrlKBqInJ3DCzkHmorr+3vcum/9q8p1IB30HVH4HV/D259jdLXvOEJwWgSca68xjyp1zIac6JYjyZj6/9JiAr2tmslJQwIgI+Sm9wRUfcwsJD7qThjbhkd3gRUFXT9PjKvNoJOG6HH57qwowrwiKDT0GTEiWK9XSvpbHnrJ3TLBOAmrb9lkztzK2lomD+85JwsTUSdx8BC7ksUAYMeqK8C6q9ed1xp5/xV8+okU+s9TTpNkLcxgtPeEXztWrWmz+/wq29owpEi3bWVSYU6lOgbWl2nUsgwIiLA1kZKigpEdD8fzochonYxsBBdTxSBprq2w8z1web60NPcegfazhPMoaVTQafFyI46EJC7bsulVN9gG4GxjsboG5pbXafx9rJN5jUHGQ1CA7jJHRGZMbAQOVNTffsjN20eVebRnsaa7n2uKsCBUZ0WIzsKVfe/s4NMJhEXr9ThUGEVDhZWWTa506OxufUmd+EatW1Sb3JkIBIiNQhQc5M7Ik/EwELkCpobgYYqB4KO5Wj5GIOu8PLtYCJyB6M6Xt7O+NY2TUYT8kuqba2kw5ZN7trY4w5D+vvatZKGhftDpejbrTQiujEGFqK+zNhsnqdTd8XBoFMFiK1HNDqtUyuv2jiUfp2ekFxraMbRSzocLtLhoCXIFF1t3XLzkgsYFh5geV6SeZ+Ywf25yR2Ru2FgIfJEJpNlQrKDIzr1V6/td9MlQjuBpY1zbVwnAhAhQBRFiKLl76II8br3C5b3ywQBggDILPdqf1Jv5z6/zescudahe3byZHfqlCmAoBig/1Cgf7zln0PNO0hzAjS5GEd+f7vurD4icoxMZhkRCQQQ0/n3iaJ5vk2Hk5HbOn8FMDYCEM336NRntT4loI1fxR39XhXbvg+1UFMKFP5gf07pD/S/yRxiQm66FmYCB/HZXdQnMLAQeTpBMO8crPIHAgd2/n2iaJ6QbKhu68W2r2/7Rje8ttlkwoXKWpy4rMfxYj1OFOtxtqwWRlPrFtiAIG8MD/fHsDB/DI8IwE3aAKi9ZO18vnPr7FC3Pr+dz2nr2uYGoPIMUJ4PlJ8EKk6Z/95YDVzabz5aUngDIbEtRmPizUdQjEuvVCPPw5YQEfVJDU1GHLusx+EWT64+X9F6kzu5TMBNWn8kR2mQaNmt9yatHxSetMmdsQm4cs4cYMpPWf6Zbw4zRkPb75F5Af1iW4QYy6hMv1hJVqKRe+IcFiLySLq6Jhy+dC3AHCysQnl161/Iai8ZEiJaPi9Jg4HBHrjJnckIXL1gDi7WEGMNNU2twx8AQJABwYOBkKH282RC4syPviByAAMLERHMk3dL9A04VKizbHJXhcOFOlQbWk8y9lcpEOjrBV+lAv5qBfxUCviqrv3ZT+UFX5Xc8vc2/mz5p1uM3JhMgP5Si7ZS/rU/d7TsPnBgiwDTYmRGrem92qlPYWAhImqHySTiXEWtrZV0sEiHE5f1aDR2Y0l4C95ecrug46uSw0/l1WYI8lWZ/+mvtv+zn0oBH6Xc9UZ8RNE8off61lL5SaCuov33+UdcW63Ucp6MT3Dv1U4uiYGFiMgBjc0mFFypRXVDM2oMzahpaEa1oRm1lj/XGFocLV9r8bqhjV19u0MQAD+lAn7XhRlfy7m2gk57r6kUsp4PP7WVlpEYa4ixHNWX23+PT8h1k30t82T8tFyC7SEYWIiIelljs+laiLkuzLT558YW564LRca2tgPuBi+5YAsvtkPddtBp+ZrddV1teTXoro3GtGwtdfTUdbWmjdbSUEATySDjZhhYiIj6KFEUYWg22Y32XAszTagxGC3nmlBrMFqua7K8bkRNg/nPtQYjatqYq9Ndai8Z/FRe8FPJ7YONLdxYXlMp4Ke2/tmrVQjyERsgu9Ji+XV5vjnQXDnX/o7NSj/z5N6WozIhNwFB0X3+qeieioGFiIhgMomobbSGlyZbCKo1NNv/2RKMrCNELV+z/r0nWl6+ymsBxlelME989jIhWlaMQcZCDGgugNZwEf3qzyGg9iJkYjsBTK6ybIZ3XWspeDAg54M1XRl3uiUiIshkAvzVXvBXewFQd+teTUZTp4KO7c9tvWZpgzWbzI9hsI4cQX/9p6kAxFoOMwWaMUgoRaxwGbHCJcTJihAnXMIQ4TLURgNQesR8tGCEHJXqKFz1iYHebwjqA2PRGHQT0C8WPr6+8G8x8uOv7qW5PtRlHGEhIqJeY215tWx3XR90zMGojZZXw7UgVGNoRl2jETKYMEAoR5xwyXzILiFWMB9+QkObNRhFAQViKM6IkTgtDsAZUwROi5G4KAyATO1na135qxXwV3vZRoH825j/43dd6HHZFV4uii0hIiJye81GE2objS3Cz7W2V3V9E0TdJaiqTsNPfxaBtefQr/48whovws/U1uMkzIrEEJwxDcBpcYAlzAzAGXEA9Oj8pngyAbYWl23ujtqrncDT8u9edud8lQq3f0I5AwsREVFbRBGoLbfbQ0YsOwmx4hRktWXtvq1GGYJydTSKvaJRoIjCBZgDzaVGP7t5P85e4eWrlDsYeqwTn+1f83LRDQ0ZWIiIiBxVd6X1qqXyfPOuv+3x6WdbrST2H4rGoJtQHTAEenk/1DQaW7WxroWbJruW2PX7/DQ6eZKzSiFrFW78VF4IULccBWo5KtQ69PipFFB7OXc1FgMLERGRszToLc9byrff3beqAO0+SVulsaxWGgoMSAFSMh36SEOz0by6q6EZ1S3CTcuAU93Q1GYganmuvsnY/e9vERXsjW9fuNNp9wO4SoiIiMh51AFAZIr5aKmxDqg8fV2QsewlY9ABRfvMx5XzDgcWlUIOlUKOYF9lt0pvNprMk5fbmbhs//cm+xGflv9sbIafStol4gwsREREXaH0AcKTzEdLzQag8qxld99TgH+4NPUBUMhl0PjIoPHpXtgwmUSn78XjKAYWIiIiZ1KoAO1w8+EmZDIB3kppdxN2zWnDRERERC0wsBAREZHLY2AhIiIil8fAQkRERC6PgYWIiIhcHgMLERERuTwGFiIiInJ5DCxERETk8hhYiIiIyOUxsBAREZHLY2AhIiIil8fAQkRERC6PgYWIiIhcnls8rVkURQCAXq+XuBIiIiLqLOvvbevv8Y64RWCprq4GAERFRUlcCRERETmquroaGo2mw2sEsTOxxsWZTCZcvnwZ/v7+EATBqffW6/WIiopCYWEhAgICnHpvchx/Hq6FPw/Xwp+H6+HPpGOiKKK6uhoRERGQyTqepeIWIywymQyRkZE9+hkBAQH8l82F8OfhWvjzcC38ebge/kzad6ORFStOuiUiIiKXx8BCRERELo+B5QZUKhWWLFkClUoldSkE/jxcDX8eroU/D9fDn4nzuMWkWyIiInJvHGEhIiIil8fAQkRERC6PgYWIiIhcHgMLERERuTwGlg6sWrUK0dHRUKvVSE1Nxd69e6UuyWNlZ2dj9OjR8Pf3R2hoKO6//37k5+dLXRZZvPbaaxAEAc8++6zUpXisS5cuYfbs2ejXrx+8vb0xcuRI/Pe//5W6LI9kNBqxaNEixMTEwNvbG0OGDMHSpUs79bwcah8DSzs2bdqErKwsLFmyBAcOHEBSUhLS09NRVlYmdWkeadeuXXjqqafwww8/YPv27WhqasLEiRNRW1srdWkeb9++ffjb3/6GxMREqUvxWFevXsX48ePh5eWFbdu24fjx4/jTn/6EoKAgqUvzSK+//jpWr16Nt99+GydOnMDrr7+O5cuX46233pK6tD6Ny5rbkZqaitGjR+Ptt98GYH5eUVRUFJ5++mksWLBA4uqovLwcoaGh2LVrF26//Xapy/FYNTU1uOWWW/DXv/4Vr7zyCpKTk7Fy5Uqpy/I4CxYswHfffYdvv/1W6lIIwD333AOtVou1a9fazj344IPw9vbGhx9+KGFlfRtHWNrQ2NiI/fv3Iy0tzXZOJpMhLS0NeXl5ElZGVjqdDgAQHBwscSWe7amnnsKUKVPs/rtCve/f//43UlJS8NBDDyE0NBQ333wz3n33XanL8ljjxo1Dbm4uTp06BQA4dOgQdu/ejYyMDIkr69vc4uGHzlZRUQGj0QitVmt3XqvV4uTJkxJVRVYmkwnPPvssxo8fj4SEBKnL8ViffPIJDhw4gH379kldisc7d+4cVq9ejaysLPzud7/Dvn378Jvf/AZKpRJz5syRujyPs2DBAuj1esTHx0Mul8NoNGLZsmWYNWuW1KX1aQws1Oc89dRTOHr0KHbv3i11KR6rsLAQzzzzDLZv3w61Wi11OR7PZDIhJSUFr776KgDg5ptvxtGjR7FmzRoGFgn84x//wMaNG/HRRx9hxIgROHjwIJ599llERETw59ENDCxtCAkJgVwuR2lpqd350tJShIWFSVQVAcCvf/1rfP755/jmm28QGRkpdTkea//+/SgrK8Mtt9xiO2c0GvHNN9/g7bffhsFggFwul7BCzxIeHo7hw4fbnRs2bBj++c9/SlSRZ3v++eexYMECzJgxAwAwcuRIXLx4EdnZ2Qws3cA5LG1QKpUYNWoUcnNzbedMJhNyc3MxduxYCSvzXKIo4te//jU2b96Mr776CjExMVKX5NHuuusuHDlyBAcPHrQdKSkpmDVrFg4ePMiw0svGjx/fapn/qVOnMGjQIIkq8mx1dXWQyex/vcrlcphMJokqcg8cYWlHVlYW5syZg5SUFIwZMwYrV65EbW0tMjMzpS7NIz311FP46KOP8H//93/w9/dHSUkJAECj0cDb21vi6jyPv79/q/lDvr6+6NevH+cVSeB//ud/MG7cOLz66qv4+c9/jr179+Kdd97BO++8I3VpHmnq1KlYtmwZBg4ciBEjRuDHH3/EihUrMG/ePKlL69tEatdbb70lDhw4UFQqleKYMWPEH374QeqSPBaANo/33ntP6tLI4qc//an4zDPPSF2Gx/rPf/4jJiQkiCqVSoyPjxffeecdqUvyWHq9XnzmmWfEgQMHimq1Whw8eLD44osvigaDQerS+jTuw0JEREQuj3NYiIiIyOUxsBAREZHLY2AhIiIil8fAQkRERC6PgYWIiIhcHgMLERERuTwGFiIiInJ5DCxERETk8hhYiIiIyOUxsBAREZHLY2AhIiIil8fAQkRERC7v/wMx33Olw3E8QgAAAABJRU5ErkJggg==\n"
- },
- "metadata": {}
- }
- ]
+ "layout": "IPY_MODEL_d7150457f96049c88b5a7e2604670830"
+ }
+ },
+ "ff6205ce16e447368fe2f232db393de0": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
}
- ]
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
}
diff --git a/Workshop3/molcal_workshop3.ipynb b/Workshop3/molcal_workshop3.ipynb
index 4228bc38304f60847328921ff68667d019cf3850..11b350a3da132975effeb6e8f9f864af3c998251 100644
--- a/Workshop3/molcal_workshop3.ipynb
+++ b/Workshop3/molcal_workshop3.ipynb
@@ -684,13 +684,7 @@
"GPU available: True (mps), used: False\n",
"TPU available: False, using: 0 TPU cores\n",
"IPU available: False, using: 0 IPUs\n",
- "HPU available: False, using: 0 HPUs\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
+ "HPU available: False, using: 0 HPUs\n",
"\n",
" | Name | Type | Params\n",
"--------------------------------------------\n",
@@ -960,7 +954,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "molcal",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -974,9 +968,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.13"
+ "version": "3.9.7"
}
},
"nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
}