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dc.contributor.author한상수-
dc.contributor.author김동훈-
dc.contributor.author김정래-
dc.contributor.authorLeslie-
dc.date.accessioned2021-09-05T15:30:02Z-
dc.date.available2021-09-05T15:30:02Z-
dc.date.issued2021-09-
dc.identifier.citationVOL 12-8383-
dc.identifier.issn1948-7185-
dc.identifier.other57310-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/73747-
dc.publisherJournal of Physical Chemistry Letters-
dc.subjectDeep learning-
dc.subjectMaterial properties-
dc.subjectChemical composition-
dc.subjectDiffraction pattern-
dc.subjectElement vector-
dc.titleDeep Learning-Based Predictions of Material Properties Using Chemical Compositions and Diffraction Patterns as Experimentally Accessible Inputs-
dc.typeArticle-
dc.relation.page83768383-
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