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

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

<jats:title>Abstract</jats:title><jats:p>This 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 sy...

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Bibliographic Details
Main Authors: Batzner, Simon, Musaelian, Albert, Sun, Lixin, Geiger, Mario, Mailoa, Jonathan P, Kornbluth, Mordechai, Molinari, Nicola, Smidt, Tess E, Kozinsky, Boris
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Format: Article
Language:English
Published: Springer Science and Business Media LLC 2022
Online Access:https://hdl.handle.net/1721.1/143778

Internet

https://hdl.handle.net/1721.1/143778

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