SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

• URL: http://arxiv.org/abs/2101.03164v1
• Date: Fri, 8 Jan 2021 18:49:10 GMT
• Title: SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• Authors: Simon Batzner, Tess E. Smidt, Lixin Sun, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, and Boris Kozinsky
• Abstract summary: NequIP is a SE(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. The method achieves state-of-the-art accuracy on a challenging set of diverse molecules and materials while exhibiting remarkable data efficiency.
• Score: 0.17590081165362778
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents Neural Equivariant Interatomic Potentials (NequIP), a SE(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs SE(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging set of diverse molecules and materials while exhibiting remarkable data efficiency. 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. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

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