diff --git a/Workshop3/Molcal_workshop3_materials_property_prediction.ipynb b/Workshop3/Molcal_workshop3_materials_property_prediction.ipynb
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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",
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+ " <td>3</td>\n",
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+ " <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",
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+ " </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