With slight changes of the initialization and hyperparameters, we achieve improved RMSE on the 3BPA test set! This work serves as a simple introduction to training and testing equivariant neural network potentials in JAX, and can be easily extended for new methodologies.

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In order to verify correctness of the implementation, we compare performance on the 3BPA dataset to two different PyTorch NequIP implementations: (1) @simonbatzner.bsky.social
et al.'s "nequip" repo, and (2) @ilyesbatatia.bsky.social
et al.'s "mace" repo...

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The package includes code for training and evaluation of neural network potentials, a calculator for usage with Atomic Simulation Environment (ASE), and pre-trained weights for the 3BPA dataset.

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