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Uncertainty quantification for molecular belongings predictions with graph neural structure seek

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D4DD00088A, Paper Shengli Jiang, Shiyi Qin, Reid C. Van Lehn, Prasanna Balaprakash, Victor M. ZavalaAutoGNNUQ employs neural architecture search to enhance uncertainty quantification for molecular property prediction via graph neural networks.To cite this article before page numbers are assigned, use the

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Room-Temperature-Modulated Polymorphism of Nonfullerene Acceptors Permit Environment friendly Bilayer Natural Sun Cells

Energy Environ. Sci., 2024, Accepted ManuscriptDOI: 10.1039/D4EE02330G, Paper Zhenmin Zhao, Sein Chung, Young Yong Kim, Minyoung Jeong, Xin Li, Jingjing Zhao, Chaofeng Zhu, Safakath Karuthedath, Yufei Zhong, Kilwon Cho, Zhipeng KanPolymorphism of nonfullerene acceptors enhances electron transport properties and potentially impacts the performance of organic electronic

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