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Liu, Yufeng, Li, Zhenyu (2023) Predict Ionization Energy of Molecules Using Conventional and Graph-Based Machine Learning Models. Journal of Chemical Information and Modeling, 63 (3) 806-814 doi:10.1021/acs.jcim.2c01321

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Reference TypeJournal (article/letter/editorial)
TitlePredict Ionization Energy of Molecules Using Conventional and Graph-Based Machine Learning Models
JournalJournal of Chemical Information and Modeling
AuthorsLiu, YufengAuthor
Li, ZhenyuAuthor
Year2023 (February 13)Volume63
Issue3
PublisherAmerican Chemical Society (ACS)
DOIdoi:10.1021/acs.jcim.2c01321Search in ResearchGate
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Mindat Ref. ID15705432Long-form Identifiermindat:1:5:15705432:5
GUID0
Full ReferenceLiu, Yufeng, Li, Zhenyu (2023) Predict Ionization Energy of Molecules Using Conventional and Graph-Based Machine Learning Models. Journal of Chemical Information and Modeling, 63 (3) 806-814 doi:10.1021/acs.jcim.2c01321
Plain TextLiu, Yufeng, Li, Zhenyu (2023) Predict Ionization Energy of Molecules Using Conventional and Graph-Based Machine Learning Models. Journal of Chemical Information and Modeling, 63 (3) 806-814 doi:10.1021/acs.jcim.2c01321
In(2023, February) Journal of Chemical Information and Modeling Vol. 63 (3) American Chemical Society (ACS)

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