PolyID™ (Polymer Inverse Design) [SWR-21-98]

Abstract

PolyID™ provides a framework for building, training, and predicting polymer properties using graph neural networks. The codes leverages Neural Fingerprint (nfp) (See DOE Code ID 22185) for building tensorflow-based message-passing neural networks, and Monomers to Polymers (m2p) (See DOE Code ID 44795) for building polymer structures. To understand and use the pipeline, the following examples have been provided: Building polymer structures: examples/example_generate_polymer_structures.ipynb Checking domain of validity: examples/example_determine_domain-of-validity.ipynb Training a graph neural network: examples/example_generate_and_train_models.ipynb Predicting with the trained model: examples/example_predict_with_trained_models.ipynb