Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

Cheol Woo Park, Mordechai Kornbluth, Jonathan Vandermause, Chris Wolverton, Boris Kozinsky*, Jonathan P. Mailoa

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li7P3S11, a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.

Original languageEnglish (US)
Article number73
Journalnpj Computational Materials
Volume7
Issue number1
DOIs
StatePublished - Dec 2021

ASJC Scopus subject areas

  • Modeling and Simulation
  • Materials Science(all)
  • Mechanics of Materials
  • Computer Science Applications

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