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

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dc.authoridMikami, Masashi/0000-0002-1866-824X
dc.authoridTamura, Tomoyuki/0000-0001-5201-0292
dc.contributor.authorMiyazaki, Hidetoshi
dc.contributor.authorTamura, Tomoyuki
dc.contributor.authorMikami, Masashi
dc.contributor.authorWatanabe, Kosuke
dc.contributor.authorIde, Naoki
dc.contributor.authorOzkendir, Osman Murat
dc.contributor.authorNishino, Yoichi
dc.date.accessioned2025-03-17T12:25:54Z
dc.date.available2025-03-17T12:25:54Z
dc.date.issued2021
dc.departmentTarsus Üniversitesi
dc.description.abstractHalf-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.
dc.description.sponsorshipJapan Society for the Promotion of Science's (JSPS) [17K06771, 18K04700, 18K04748, 20K05100, 20K05060]; Grants-in-Aid for Scientific Research [20K05100, 20K05060, 18K04748, 17K06771, 18K04700] Funding Source: KAKEN
dc.description.sponsorshipThe computations were performed by the Research Center for Computational Science, Okazaki, Japan. This study was partly supported by the Japan Society for the Promotion of Science's (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) (C) (17K06771, 18K04700, 18K04748, 20K05100, 20K05060). We would like to thank Editage for English language editing.
dc.identifier.doi10.1038/s41598-021-92030-4
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.pmid34183699
dc.identifier.scopus2-s2.0-85109770226
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1038/s41598-021-92030-4
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1915
dc.identifier.volume11
dc.identifier.wosWOS:000669978600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherNature Portfolio
dc.relation.ispartofScientific Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250316
dc.subjectMolecular-Dynamics Simulation
dc.subjectTotal-Energy Calculations
dc.subjectThermoelectric Properties
dc.subjectOptimization
dc.titleMachine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information
dc.typeArticle

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