This repository contains all the work that is being done during the Mitacs GRI'23. The project was mostly based on implementation of State of the Art Graph Neural Networks for prediction of various material properties as well as generation of nouvel materials which seems to be possible via intensive Density Functional Theory (DFT) Calculations. The graph neural networks used here includes a custom GNN, MEGNet-16 and CGCNN.
The repository includes various models from feedforward nets to sequence models for trying out bandgap prediction of Zincblend and rocksalt strcutures__ using features like molecule properties (local representations) as well as distributed representations (like embeddings).
This also includes the XTB dataset for property predicion from Morgan fingerprints of organic molecules.
Every folder has a bash script called run.sh:
chmod +x run.sh
./run.sh