Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

Gaining insights into combustion processes is challenging due to the complex reactions involved. The present work proposes a neural network potential model trained to ab initio data that enables to simulate the combustion of methane by predicting reactants, products and reaction intermediates.

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Autores principales:	Jinzhe Zeng, Liqun Cao, Mingyuan Xu, Tong Zhu, John Z. H. Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/21133458a20b4ec9a9fdc67225b9d5a7
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spelling	oai:doaj.org-article:21133458a20b4ec9a9fdc67225b9d5a72021-12-02T15:33:50ZComplex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation10.1038/s41467-020-19497-z2041-1723https://doaj.org/article/21133458a20b4ec9a9fdc67225b9d5a72020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19497-zhttps://doaj.org/toc/2041-1723Gaining insights into combustion processes is challenging due to the complex reactions involved. The present work proposes a neural network potential model trained to ab initio data that enables to simulate the combustion of methane by predicting reactants, products and reaction intermediates.Jinzhe ZengLiqun CaoMingyuan XuTong ZhuJohn Z. H. ZhangNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Jinzhe Zeng Liqun Cao Mingyuan Xu Tong Zhu John Z. H. Zhang Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation
description	Gaining insights into combustion processes is challenging due to the complex reactions involved. The present work proposes a neural network potential model trained to ab initio data that enables to simulate the combustion of methane by predicting reactants, products and reaction intermediates.
format	article
author	Jinzhe Zeng Liqun Cao Mingyuan Xu Tong Zhu John Z. H. Zhang
author_facet	Jinzhe Zeng Liqun Cao Mingyuan Xu Tong Zhu John Z. H. Zhang
author_sort	Jinzhe Zeng
title	Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation
title_short	Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation
title_full	Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation
title_fullStr	Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation
title_full_unstemmed	Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation
title_sort	complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/21133458a20b4ec9a9fdc67225b9d5a7
work_keys_str_mv	AT jinzhezeng complexreactionprocessesincombustionunraveledbyneuralnetworkbasedmoleculardynamicssimulation AT liquncao complexreactionprocessesincombustionunraveledbyneuralnetworkbasedmoleculardynamicssimulation AT mingyuanxu complexreactionprocessesincombustionunraveledbyneuralnetworkbasedmoleculardynamicssimulation AT tongzhu complexreactionprocessesincombustionunraveledbyneuralnetworkbasedmoleculardynamicssimulation AT johnzhzhang complexreactionprocessesincombustionunraveledbyneuralnetworkbasedmoleculardynamicssimulation
_version_	1718387010708701184

Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

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