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Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds

dc.contributor.authorGeorgescu, Alexandru B
dc.contributor.authorRen, Peiwen
dc.contributor.authorToland, Aubrey R
dc.contributor.authorZhang, Shengtong
dc.contributor.authorMiller, Kyle D
dc.contributor.authorApley, Daniel W
dc.contributor.authorOlivetti, Elsa A
dc.contributor.authorWagner, Nicholas
dc.contributor.authorRondinelli, James M
dc.date.accessioned2022-05-18T15:52:47Z
dc.date.available2022-05-18T15:52:47Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/142583
dc.language.isoen
dc.publisherAmerican Chemical Society (ACS)en_US
dc.relation.isversionof10.1021/ACS.CHEMMATER.1C00905en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licensen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceAmerican Chemical Societyen_US
dc.titleDatabase, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compoundsen_US
dc.typeArticleen_US
dc.identifier.citationGeorgescu, Alexandru B, Ren, Peiwen, Toland, Aubrey R, Zhang, Shengtong, Miller, Kyle D et al. 2021. "Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds." Chemistry of Materials, 33 (14).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Materials Science and Engineering
dc.relation.journalChemistry of Materialsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-05-18T15:47:04Z
dspace.orderedauthorsGeorgescu, AB; Ren, P; Toland, AR; Zhang, S; Miller, KD; Apley, DW; Olivetti, EA; Wagner, N; Rondinelli, JMen_US
dspace.date.submission2022-05-18T15:47:06Z
mit.journal.volume33en_US
mit.journal.issue14en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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