Neural network reactive force field for C, H, N, and O systems

Abstract Reactive force fields have enabled an atomic level description of a wide range of phenomena, from chemistry at extreme conditions to the operation of electrochemical devices and catalysis. While significant insight and semi-quantitative understanding have been drawn from such work, the accu...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Pilsun Yoo, Michael Sakano, Saaketh Desai, Md Mahbubul Islam, Peilin Liao, Alejandro Strachan
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/29cd9e0fc0e54e598944fa305adc4586
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:29cd9e0fc0e54e598944fa305adc4586
record_format	dspace
spelling	oai:doaj.org-article:29cd9e0fc0e54e598944fa305adc45862021-12-02T10:49:15ZNeural network reactive force field for C, H, N, and O systems10.1038/s41524-020-00484-32057-3960https://doaj.org/article/29cd9e0fc0e54e598944fa305adc45862021-01-01T00:00:00Zhttps://doi.org/10.1038/s41524-020-00484-3https://doaj.org/toc/2057-3960Abstract Reactive force fields have enabled an atomic level description of a wide range of phenomena, from chemistry at extreme conditions to the operation of electrochemical devices and catalysis. While significant insight and semi-quantitative understanding have been drawn from such work, the accuracy of reactive force fields limits quantitative predictions. We developed a neural network reactive force field (NNRF) for CHNO systems to describe the decomposition and reaction of the high-energy nitramine 1,3,5-trinitroperhydro-1,3,5-triazine (RDX). NNRF was trained using energies and forces of a total of 3100 molecules (11,941 geometries) and 15 condensed matter systems (32,973 geometries) obtained from density functional theory calculations with semi-empirical corrections to dispersion interactions. The training set is generated via a semi-automated iterative procedure that enables refinement of the NNRF until a desired accuracy is attained. The root mean square (RMS) error of NNRF on a testing set of configurations describing the reaction of RDX is one order of magnitude lower than current state of the art potentials.Pilsun YooMichael SakanoSaaketh DesaiMd Mahbubul IslamPeilin LiaoAlejandro StrachanNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-10 (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 Pilsun Yoo Michael Sakano Saaketh Desai Md Mahbubul Islam Peilin Liao Alejandro Strachan Neural network reactive force field for C, H, N, and O systems
description	Abstract Reactive force fields have enabled an atomic level description of a wide range of phenomena, from chemistry at extreme conditions to the operation of electrochemical devices and catalysis. While significant insight and semi-quantitative understanding have been drawn from such work, the accuracy of reactive force fields limits quantitative predictions. We developed a neural network reactive force field (NNRF) for CHNO systems to describe the decomposition and reaction of the high-energy nitramine 1,3,5-trinitroperhydro-1,3,5-triazine (RDX). NNRF was trained using energies and forces of a total of 3100 molecules (11,941 geometries) and 15 condensed matter systems (32,973 geometries) obtained from density functional theory calculations with semi-empirical corrections to dispersion interactions. The training set is generated via a semi-automated iterative procedure that enables refinement of the NNRF until a desired accuracy is attained. The root mean square (RMS) error of NNRF on a testing set of configurations describing the reaction of RDX is one order of magnitude lower than current state of the art potentials.
format	article
author	Pilsun Yoo Michael Sakano Saaketh Desai Md Mahbubul Islam Peilin Liao Alejandro Strachan
author_facet	Pilsun Yoo Michael Sakano Saaketh Desai Md Mahbubul Islam Peilin Liao Alejandro Strachan
author_sort	Pilsun Yoo
title	Neural network reactive force field for C, H, N, and O systems
title_short	Neural network reactive force field for C, H, N, and O systems
title_full	Neural network reactive force field for C, H, N, and O systems
title_fullStr	Neural network reactive force field for C, H, N, and O systems
title_full_unstemmed	Neural network reactive force field for C, H, N, and O systems
title_sort	neural network reactive force field for c, h, n, and o systems
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/29cd9e0fc0e54e598944fa305adc4586
work_keys_str_mv	AT pilsunyoo neuralnetworkreactiveforcefieldforchnandosystems AT michaelsakano neuralnetworkreactiveforcefieldforchnandosystems AT saakethdesai neuralnetworkreactiveforcefieldforchnandosystems AT mdmahbubulislam neuralnetworkreactiveforcefieldforchnandosystems AT peilinliao neuralnetworkreactiveforcefieldforchnandosystems AT alejandrostrachan neuralnetworkreactiveforcefieldforchnandosystems
_version_	1718396577736818688

Neural network reactive force field for C, H, N, and O systems

Ejemplares similares