polyid 0.1.2.2

Methods to train message passing neural network models on polymer structures.

PolyID^TM provides a framework for building, training, and predicting polymer properities using graph neural networks. The codes leverages nfp, for building tensorflow-based message-passing neural networ, and m2p, for building polymer structures. The notebooks have been provided that demonstrate how to: (1) build polymer structures from a polymer database and split into a training/validation and test set, (2) train a message passing neural network from using the trainining/validation set, and (3) evaluate the trained network on the test set. These three notebooks follow the methodology used in the forthcoming publication.

1. Building polymer structures: examples/1_generate_polymer_structures.ipynb
2. Training a message passing neural network: examples/2_generate_and_train_models.ipynb
3. Predicting and evaluating a trained network: examples/3_evaluate_model_performance_and_DoV.ipynb

Additional notebooks have been provided to provide more examples and capabilities of the PolyID code base.

4. Checking domain of validity: examples/example_determine_domain-of-validity.ipynb
5. Generating hierarchical fingerprints for performance comparison: examples/example_hierarchical_fingerprints.ipynb
6. Predicting with the trained model: examples/example_predict_with_trained_models.ipynb

Details for the methods are forthcoming in an upcoming manuscript.