Quantitative prediction of grain boundary thermal conductivities from local atomic environments

Connecting grain boundary structures to macroscopic thermal behaviour is an important step in materials analysis and design. Here the authors develop a physical model combined with a machine-learning approach to accurately predict thermal conductivities of various types of MgO grain boundaries.

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Autores principales:	Susumu Fujii, Tatsuya Yokoi, Craig A. J. Fisher, Hiroki Moriwake, Masato Yoshiya
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/c7fe7ca741a14e9ea17673a391810703
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spelling	oai:doaj.org-article:c7fe7ca741a14e9ea17673a3918107032021-12-02T16:49:43ZQuantitative prediction of grain boundary thermal conductivities from local atomic environments10.1038/s41467-020-15619-92041-1723https://doaj.org/article/c7fe7ca741a14e9ea17673a3918107032020-04-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-15619-9https://doaj.org/toc/2041-1723Connecting grain boundary structures to macroscopic thermal behaviour is an important step in materials analysis and design. Here the authors develop a physical model combined with a machine-learning approach to accurately predict thermal conductivities of various types of MgO grain boundaries.Susumu FujiiTatsuya YokoiCraig A. J. FisherHiroki MoriwakeMasato YoshiyaNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Susumu Fujii Tatsuya Yokoi Craig A. J. Fisher Hiroki Moriwake Masato Yoshiya Quantitative prediction of grain boundary thermal conductivities from local atomic environments
description	Connecting grain boundary structures to macroscopic thermal behaviour is an important step in materials analysis and design. Here the authors develop a physical model combined with a machine-learning approach to accurately predict thermal conductivities of various types of MgO grain boundaries.
format	article
author	Susumu Fujii Tatsuya Yokoi Craig A. J. Fisher Hiroki Moriwake Masato Yoshiya
author_facet	Susumu Fujii Tatsuya Yokoi Craig A. J. Fisher Hiroki Moriwake Masato Yoshiya
author_sort	Susumu Fujii
title	Quantitative prediction of grain boundary thermal conductivities from local atomic environments
title_short	Quantitative prediction of grain boundary thermal conductivities from local atomic environments
title_full	Quantitative prediction of grain boundary thermal conductivities from local atomic environments
title_fullStr	Quantitative prediction of grain boundary thermal conductivities from local atomic environments
title_full_unstemmed	Quantitative prediction of grain boundary thermal conductivities from local atomic environments
title_sort	quantitative prediction of grain boundary thermal conductivities from local atomic environments
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/c7fe7ca741a14e9ea17673a391810703
work_keys_str_mv	AT susumufujii quantitativepredictionofgrainboundarythermalconductivitiesfromlocalatomicenvironments AT tatsuyayokoi quantitativepredictionofgrainboundarythermalconductivitiesfromlocalatomicenvironments AT craigajfisher quantitativepredictionofgrainboundarythermalconductivitiesfromlocalatomicenvironments AT hirokimoriwake quantitativepredictionofgrainboundarythermalconductivitiesfromlocalatomicenvironments AT masatoyoshiya quantitativepredictionofgrainboundarythermalconductivitiesfromlocalatomicenvironments
_version_	1718383239707492352

Quantitative prediction of grain boundary thermal conductivities from local atomic environments

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