Teddy Koker

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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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.

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Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems We present finite-range embeddings (FiRE), a novel wave function ansatz for accurate large-scale ab-initio electronic structure calculations. Compared to contemporary neural-network wave functions, Fi...

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