E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
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s41467-022-29939-5.pdf
Description
Published version
Size
1.44 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Batzner, Simon
Musaelian, Albert
Sun, Lixin
Geiger, Mario
Mailoa, Jonathan P
Kornbluth, Mordechai
Molinari, Nicola
Smidt, Tess E
Kozinsky, Boris
Date Issued
December 2022
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Batzner, Simon, Musaelian, Albert, Sun, Lixin, Geiger, Mario, Mailoa, Jonathan P et al. 2022. "E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials." Nature Communications, 13 (1).
Version
Final published version
Abstract
AbstractThis work presents Neural Equivariant Interatomic Potentials (NequIP), an E(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 E(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 and diverse set of 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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41467-022-29939-5
