Predicting phase behavior of grain boundaries with evolutionary search and machine learning

The atomic structure of grain boundary phases remains unknown and is difficult to investigate experimentally. Here, the authors use an evolutionary algorithm to computationally explore interface structures in higher dimensions and predict low-energy configurations, showing interface phases may be ub...

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Main Authors:	Qiang Zhu, Amit Samanta, Bingxi Li, Robert E. Rudd, Timofey Frolov
Format:	article
Language:	EN
Published:	Nature Portfolio 2018
Subjects:	Science Q
Online Access:	https://doaj.org/article/d4cc71fc2b0342fbb5d2ec4dbcad7d19
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id	oai:doaj.org-article:d4cc71fc2b0342fbb5d2ec4dbcad7d19
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spelling	oai:doaj.org-article:d4cc71fc2b0342fbb5d2ec4dbcad7d192021-12-02T17:31:13ZPredicting phase behavior of grain boundaries with evolutionary search and machine learning10.1038/s41467-018-02937-22041-1723https://doaj.org/article/d4cc71fc2b0342fbb5d2ec4dbcad7d192018-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-02937-2https://doaj.org/toc/2041-1723The atomic structure of grain boundary phases remains unknown and is difficult to investigate experimentally. Here, the authors use an evolutionary algorithm to computationally explore interface structures in higher dimensions and predict low-energy configurations, showing interface phases may be ubiquitous.Qiang ZhuAmit SamantaBingxi LiRobert E. RuddTimofey FrolovNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-9 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Qiang Zhu Amit Samanta Bingxi Li Robert E. Rudd Timofey Frolov Predicting phase behavior of grain boundaries with evolutionary search and machine learning
description	The atomic structure of grain boundary phases remains unknown and is difficult to investigate experimentally. Here, the authors use an evolutionary algorithm to computationally explore interface structures in higher dimensions and predict low-energy configurations, showing interface phases may be ubiquitous.
format	article
author	Qiang Zhu Amit Samanta Bingxi Li Robert E. Rudd Timofey Frolov
author_facet	Qiang Zhu Amit Samanta Bingxi Li Robert E. Rudd Timofey Frolov
author_sort	Qiang Zhu
title	Predicting phase behavior of grain boundaries with evolutionary search and machine learning
title_short	Predicting phase behavior of grain boundaries with evolutionary search and machine learning
title_full	Predicting phase behavior of grain boundaries with evolutionary search and machine learning
title_fullStr	Predicting phase behavior of grain boundaries with evolutionary search and machine learning
title_full_unstemmed	Predicting phase behavior of grain boundaries with evolutionary search and machine learning
title_sort	predicting phase behavior of grain boundaries with evolutionary search and machine learning
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/d4cc71fc2b0342fbb5d2ec4dbcad7d19
work_keys_str_mv	AT qiangzhu predictingphasebehaviorofgrainboundarieswithevolutionarysearchandmachinelearning AT amitsamanta predictingphasebehaviorofgrainboundarieswithevolutionarysearchandmachinelearning AT bingxili predictingphasebehaviorofgrainboundarieswithevolutionarysearchandmachinelearning AT roberterudd predictingphasebehaviorofgrainboundarieswithevolutionarysearchandmachinelearning AT timofeyfrolov predictingphasebehaviorofgrainboundarieswithevolutionarysearchandmachinelearning
_version_	1718380702927421440

Predicting phase behavior of grain boundaries with evolutionary search and machine learning

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