Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information

Abstract Half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. When the half-Heusler compound is incorporated into the device, the control of high lattice thermal conductivity owing to high crystal sy...

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Autores principales:	Hidetoshi Miyazaki, Tomoyuki Tamura, Masashi Mikami, Kosuke Watanabe, Naoki Ide, Osman Murat Ozkendir, Yoichi Nishino
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/60e5cd41c9824d02be48e9272e3151ac
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spelling	oai:doaj.org-article:60e5cd41c9824d02be48e9272e3151ac2021-12-02T16:31:47ZMachine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information10.1038/s41598-021-92030-42045-2322https://doaj.org/article/60e5cd41c9824d02be48e9272e3151ac2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92030-4https://doaj.org/toc/2045-2322Abstract Half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. When the half-Heusler compound is incorporated into the device, the control of high lattice thermal conductivity owing to high crystal symmetry is a challenge for the thermal manager of the device. The calculation for the prediction of lattice thermal conductivity is an important physical parameter for controlling the thermal management of the device. We examined whether lattice thermal conductivity prediction by machine learning was possible on the basis of only the atomic information of constituent elements for thermal conductivity calculated by the density functional theory in various half-Heusler compounds. Consequently, we constructed a machine learning model, which can predict the lattice thermal conductivity with high accuracy from the information of only atomic radius and atomic mass of each site in the half-Heusler type crystal structure. Applying our results, the lattice thermal conductivity for an unknown half-Heusler compound can be immediately predicted. In the future, low-cost and short-time development of new functional materials can be realized, leading to breakthroughs in the search of novel functional materials.Hidetoshi MiyazakiTomoyuki TamuraMasashi MikamiKosuke WatanabeNaoki IdeOsman Murat OzkendirYoichi NishinoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hidetoshi Miyazaki Tomoyuki Tamura Masashi Mikami Kosuke Watanabe Naoki Ide Osman Murat Ozkendir Yoichi Nishino Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information
description	Abstract Half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. When the half-Heusler compound is incorporated into the device, the control of high lattice thermal conductivity owing to high crystal symmetry is a challenge for the thermal manager of the device. The calculation for the prediction of lattice thermal conductivity is an important physical parameter for controlling the thermal management of the device. We examined whether lattice thermal conductivity prediction by machine learning was possible on the basis of only the atomic information of constituent elements for thermal conductivity calculated by the density functional theory in various half-Heusler compounds. Consequently, we constructed a machine learning model, which can predict the lattice thermal conductivity with high accuracy from the information of only atomic radius and atomic mass of each site in the half-Heusler type crystal structure. Applying our results, the lattice thermal conductivity for an unknown half-Heusler compound can be immediately predicted. In the future, low-cost and short-time development of new functional materials can be realized, leading to breakthroughs in the search of novel functional materials.
format	article
author	Hidetoshi Miyazaki Tomoyuki Tamura Masashi Mikami Kosuke Watanabe Naoki Ide Osman Murat Ozkendir Yoichi Nishino
author_facet	Hidetoshi Miyazaki Tomoyuki Tamura Masashi Mikami Kosuke Watanabe Naoki Ide Osman Murat Ozkendir Yoichi Nishino
author_sort	Hidetoshi Miyazaki
title	Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information
title_short	Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information
title_full	Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information
title_fullStr	Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information
title_full_unstemmed	Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information
title_sort	machine learning based prediction of lattice thermal conductivity for half-heusler compounds using atomic information
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/60e5cd41c9824d02be48e9272e3151ac
work_keys_str_mv	AT hidetoshimiyazaki machinelearningbasedpredictionoflatticethermalconductivityforhalfheuslercompoundsusingatomicinformation AT tomoyukitamura machinelearningbasedpredictionoflatticethermalconductivityforhalfheuslercompoundsusingatomicinformation AT masashimikami machinelearningbasedpredictionoflatticethermalconductivityforhalfheuslercompoundsusingatomicinformation AT kosukewatanabe machinelearningbasedpredictionoflatticethermalconductivityforhalfheuslercompoundsusingatomicinformation AT naokiide machinelearningbasedpredictionoflatticethermalconductivityforhalfheuslercompoundsusingatomicinformation AT osmanmuratozkendir machinelearningbasedpredictionoflatticethermalconductivityforhalfheuslercompoundsusingatomicinformation AT yoichinishino machinelearningbasedpredictionoflatticethermalconductivityforhalfheuslercompoundsusingatomicinformation
_version_	1718383875449683968

Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information

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