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

Mojtaba Haghighatlari, Jie Li, Xingyi Guan, Oufan Zhang, Akshaya Das, Christopher J. Stein, Farnaz Heidar-Zadeh, Meili Liu, Martin Head-Gordon, Luke Bertels, Hongxia Hao, Itai Leven, Teresa Head Gordon

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

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.

Original languageEnglish
Pages (from-to)333-343
Number of pages11
JournalDigital Discovery
Volume1
Issue number3
DOIs
StatePublished - Jun 1 2022
Externally publishedYes

Fingerprint

Dive into the research topics of 'NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces'. Together they form a unique fingerprint.

Cite this