Understanding high pressure molecular hydrogen with a hierarchical machine-learned potential

Hydrogen has multiple molecular phases which are challenging to explore computationally. The authors develop a machine-learning approach, learning from reference ab initio molecular dynamics simulations, to derive a transferable hierarchical force model that provides insight into high pressure phase...

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Detalles Bibliográficos
Autores principales:	Hongxiang Zong, Heather Wiebe, Graeme J. Ackland
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/8847e5d5db8d47119e4a8c692d3ed252
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8847e5d5db8d47119e4a8c692d3ed252
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spelling	oai:doaj.org-article:8847e5d5db8d47119e4a8c692d3ed2522021-12-02T16:56:44ZUnderstanding high pressure molecular hydrogen with a hierarchical machine-learned potential10.1038/s41467-020-18788-92041-1723https://doaj.org/article/8847e5d5db8d47119e4a8c692d3ed2522020-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-18788-9https://doaj.org/toc/2041-1723Hydrogen has multiple molecular phases which are challenging to explore computationally. The authors develop a machine-learning approach, learning from reference ab initio molecular dynamics simulations, to derive a transferable hierarchical force model that provides insight into high pressure phases and the melting line of H2.Hongxiang ZongHeather WiebeGraeme J. AcklandNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Hongxiang Zong Heather Wiebe Graeme J. Ackland Understanding high pressure molecular hydrogen with a hierarchical machine-learned potential
description	Hydrogen has multiple molecular phases which are challenging to explore computationally. The authors develop a machine-learning approach, learning from reference ab initio molecular dynamics simulations, to derive a transferable hierarchical force model that provides insight into high pressure phases and the melting line of H2.
format	article
author	Hongxiang Zong Heather Wiebe Graeme J. Ackland
author_facet	Hongxiang Zong Heather Wiebe Graeme J. Ackland
author_sort	Hongxiang Zong
title	Understanding high pressure molecular hydrogen with a hierarchical machine-learned potential
title_short	Understanding high pressure molecular hydrogen with a hierarchical machine-learned potential
title_full	Understanding high pressure molecular hydrogen with a hierarchical machine-learned potential
title_fullStr	Understanding high pressure molecular hydrogen with a hierarchical machine-learned potential
title_full_unstemmed	Understanding high pressure molecular hydrogen with a hierarchical machine-learned potential
title_sort	understanding high pressure molecular hydrogen with a hierarchical machine-learned potential
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/8847e5d5db8d47119e4a8c692d3ed252
work_keys_str_mv	AT hongxiangzong understandinghighpressuremolecularhydrogenwithahierarchicalmachinelearnedpotential AT heatherwiebe understandinghighpressuremolecularhydrogenwithahierarchicalmachinelearnedpotential AT graemejackland understandinghighpressuremolecularhydrogenwithahierarchicalmachinelearnedpotential
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Understanding high pressure molecular hydrogen with a hierarchical machine-learned potential

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