Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials

Understanding local dynamical processes in materials is challenging due to the complexity of the local atomic environments. Here the authors propose a graph dynamical networks approach that is shown to learn the atomic scale dynamics in arbitrary phases and environments from molecular dynamics simul...

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Autores principales:	Tian Xie, Arthur France-Lanord, Yanming Wang, Yang Shao-Horn, Jeffrey C. Grossman
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/e95ce383366d4ae098b08194f9e7596d
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spelling	oai:doaj.org-article:e95ce383366d4ae098b08194f9e7596d2021-12-02T17:01:28ZGraph dynamical networks for unsupervised learning of atomic scale dynamics in materials10.1038/s41467-019-10663-62041-1723https://doaj.org/article/e95ce383366d4ae098b08194f9e7596d2019-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-10663-6https://doaj.org/toc/2041-1723Understanding local dynamical processes in materials is challenging due to the complexity of the local atomic environments. Here the authors propose a graph dynamical networks approach that is shown to learn the atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations.Tian XieArthur France-LanordYanming WangYang Shao-HornJeffrey C. GrossmanNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-9 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Tian Xie Arthur France-Lanord Yanming Wang Yang Shao-Horn Jeffrey C. Grossman Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
description	Understanding local dynamical processes in materials is challenging due to the complexity of the local atomic environments. Here the authors propose a graph dynamical networks approach that is shown to learn the atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations.
format	article
author	Tian Xie Arthur France-Lanord Yanming Wang Yang Shao-Horn Jeffrey C. Grossman
author_facet	Tian Xie Arthur France-Lanord Yanming Wang Yang Shao-Horn Jeffrey C. Grossman
author_sort	Tian Xie
title	Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
title_short	Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
title_full	Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
title_fullStr	Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
title_full_unstemmed	Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
title_sort	graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/e95ce383366d4ae098b08194f9e7596d
work_keys_str_mv	AT tianxie graphdynamicalnetworksforunsupervisedlearningofatomicscaledynamicsinmaterials AT arthurfrancelanord graphdynamicalnetworksforunsupervisedlearningofatomicscaledynamicsinmaterials AT yanmingwang graphdynamicalnetworksforunsupervisedlearningofatomicscaledynamicsinmaterials AT yangshaohorn graphdynamicalnetworksforunsupervisedlearningofatomicscaledynamicsinmaterials AT jeffreycgrossman graphdynamicalnetworksforunsupervisedlearningofatomicscaledynamicsinmaterials
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Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials

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