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Workshop3/outline.md

+# 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?