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College of Engineering(공과대학)Dept. of Materials Science and Engineering(신소재공학과)MS-Conference Papers(학술회의논문)

Predicting Electronic Density of States of Nanoparticles by Principal Component Analysis and Crystal Graph Convolutional Neural Network

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dc.contributor.authorBang, Kihoonko
dc.contributor.authorYeo, Byung Chulko
dc.contributor.authorHong, Doosunko
dc.contributor.authorKim, Donghunko
dc.contributor.authorHan, Sang Sooko
dc.contributor.authorLee, Hyuck-Moko
dc.date.accessioned2023-08-10T05:00:15Z-
dc.date.available2023-08-10T05:00:15Z-
dc.date.created2023-05-23-
dc.date.issued2020-02-24-
dc.identifier.citationTMS 2020 Annual Meeting & Exhibition-
dc.identifier.urihttp://hdl.handle.net/10203/311386-
dc.languageEnglish-
dc.publisherThe Minerals, Metals & Materials Society-
dc.titlePredicting Electronic Density of States of Nanoparticles by Principal Component Analysis and Crystal Graph Convolutional Neural Network-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.publicationnameTMS 2020 Annual Meeting & Exhibition-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationSan Diego, CA-
dc.contributor.localauthorLee, Hyuck-Mo-
dc.contributor.nonIdAuthorBang, Kihoon-
dc.contributor.nonIdAuthorYeo, Byung Chul-
dc.contributor.nonIdAuthorHong, Doosun-
dc.contributor.nonIdAuthorKim, Donghun-
dc.contributor.nonIdAuthorHan, Sang Soo-
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MS-Conference Papers(학술회의논문)
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