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Computational Modeling of Indoor Organic Photovoltaics: Dataset Curation, Predictive Analysis, and Machine Learning Approaches.

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Title: Computational Modeling of Indoor Organic Photovoltaics: Dataset Curation, Predictive Analysis, and Machine Learning Approaches.
Authors: Yu, Hung-Nien1, Chen, Hsu-Yuan2, Sharma, Ganesh D.3,4, Cheng, Yen-Ju5,6, Hsu, Chain-Shu5,6, Chu, Ta-Ya7, Lu, Jianping7, Chen, Fang-Chung1,6, fcchendop@nycu.edu.tw
Source: Archives of Computational Methods in Engineering; Jan2026, Vol. 33 Issue 1, p325-338, 14p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 191451278
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="JN" term="%22Archives+of+Computational+Methods+in+Engineering%22">Archives of Computational Methods in Engineering</searchLink>; Jan2026, Vol. 33 Issue 1, p325-338, 14p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=191451278
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      – Type: doi
        Value: 10.1007/s11831-025-10310-y
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 325
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      – TitleFull: Computational Modeling of Indoor Organic Photovoltaics: Dataset Curation, Predictive Analysis, and Machine Learning Approaches.
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            NameFull: Yu, Hung-Nien
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            NameFull: Cheng, Yen-Ju
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            NameFull: Chu, Ta-Ya
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              Text: Jan2026
              Type: published
              Y: 2026
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            – TitleFull: Archives of Computational Methods in Engineering
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