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.
n/n
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...
3/n
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.
2/n
Introducing nequip-eqx, a JAX implementation of the popular NequIP interatomic potential model.
Repo: github.com/teddykoker/n...
The goal of the repository is to offer a simple (<1000 lines of code) implementation while providing competitive performance to existing codebases.
1/n
I am truly excited to share our latest work with @mscherbela.bsky.social, Philipp Grohs, and @guennemann on "Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems"!
arxiv.org/abs/2504.06087
