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

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 en...

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Autores principales:	Cheol Woo Park, Mordechai Kornbluth, Jonathan Vandermause, Chris Wolverton, Boris Kozinsky, Jonathan P. Mailoa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/69d11dd21aef4871997919972e929e7b
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spelling	oai:doaj.org-article:69d11dd21aef4871997919972e929e7b2021-12-02T15:45:30ZAccurate and scalable graph neural network force field and molecular dynamics with direct force architecture10.1038/s41524-021-00543-32057-3960https://doaj.org/article/69d11dd21aef4871997919972e929e7b2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00543-3https://doaj.org/toc/2057-3960Abstract 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.Cheol Woo ParkMordechai KornbluthJonathan VandermauseChris WolvertonBoris KozinskyJonathan P. MailoaNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Cheol Woo Park Mordechai Kornbluth Jonathan Vandermause Chris Wolverton Boris Kozinsky Jonathan P. Mailoa Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
description	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.
format	article
author	Cheol Woo Park Mordechai Kornbluth Jonathan Vandermause Chris Wolverton Boris Kozinsky Jonathan P. Mailoa
author_facet	Cheol Woo Park Mordechai Kornbluth Jonathan Vandermause Chris Wolverton Boris Kozinsky Jonathan P. Mailoa
author_sort	Cheol Woo Park
title	Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
title_short	Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
title_full	Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
title_fullStr	Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
title_full_unstemmed	Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
title_sort	accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/69d11dd21aef4871997919972e929e7b
work_keys_str_mv	AT cheolwoopark accurateandscalablegraphneuralnetworkforcefieldandmoleculardynamicswithdirectforcearchitecture AT mordechaikornbluth accurateandscalablegraphneuralnetworkforcefieldandmoleculardynamicswithdirectforcearchitecture AT jonathanvandermause accurateandscalablegraphneuralnetworkforcefieldandmoleculardynamicswithdirectforcearchitecture AT chriswolverton accurateandscalablegraphneuralnetworkforcefieldandmoleculardynamicswithdirectforcearchitecture AT boriskozinsky accurateandscalablegraphneuralnetworkforcefieldandmoleculardynamicswithdirectforcearchitecture AT jonathanpmailoa accurateandscalablegraphneuralnetworkforcefieldandmoleculardynamicswithdirectforcearchitecture
_version_	1718385755627192320

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

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