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Keyword: NequIP ×Clear all

2 claims from 1 paper

1. Materials Science › Machine Learning in Materials Science

   E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

   Batzner, Musaelian, Sun et al. · Nature Communications · 2022

   The paper presents NequIP, a neural network for learning interatomic potentials from ab-initio calculations, using E(3)-equivariant convolutions to reach high accuracy with far less training data.

   Unchecked2 claims
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   1. UncheckedNequIP, a symmetry-aware neural network, reaches state-of-the-art accuracy on varied molecules and materials while needing little training data.“The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency.”
   2. Unchecked“NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets.”