Issue 3, 2022

NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces

Abstract

We report a new deep learning message passing network that takes inspiration from Newton's equations of motion to learn interatomic potentials and forces. With the advantage of directional information from trainable force vectors, and physics-infused operators that are inspired by Newtonian physics, the entire model remains rotationally equivariant, and many-body interactions are inferred by more interpretable physical features. We test NewtonNet on the prediction of several reactive and non-reactive high quality ab initio data sets including single small molecules, a large set of chemically diverse molecules, and methane and hydrogen combustion reactions, achieving state-of-the-art test performance on energies and forces with far greater data and computational efficiency than other deep learning models.

Graphical abstract: NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces

Supplementary files

Article information

Article type
Paper
Submitted
26 Feb 2022
Accepted
26 Apr 2022
First published
27 Apr 2022
This article is Open Access
Creative Commons BY-NC license

Digital Discovery, 2022,1, 333-343

NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces

M. Haghighatlari, J. Li, X. Guan, O. Zhang, A. Das, C. J. Stein, F. Heidar-Zadeh, M. Liu, M. Head-Gordon, L. Bertels, H. Hao, I. Leven and T. Head-Gordon, Digital Discovery, 2022, 1, 333 DOI: 10.1039/D2DD00008C

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