Machine learning-assisted dual-atom sites design with interpretable descriptors unifying electrocatalytic reactions.
Autor: | Lin X; School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University, Tianjin, 300072, China.; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China.; Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192, China.; National Industry-Education Platform of Energy Storage, Tianjin University, 135 Yaguan Road, Tianjin, 300350, China.; International Joint Laboratory of Low-Carbon Chemical Engineering of Ministry of Education, Tianjin, 300350, China., Du X; School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University, Tianjin, 300072, China.; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China.; Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192, China.; National Industry-Education Platform of Energy Storage, Tianjin University, 135 Yaguan Road, Tianjin, 300350, China.; International Joint Laboratory of Low-Carbon Chemical Engineering of Ministry of Education, Tianjin, 300350, China., Wu S; School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University, Tianjin, 300072, China.; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China.; Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192, China.; National Industry-Education Platform of Energy Storage, Tianjin University, 135 Yaguan Road, Tianjin, 300350, China.; International Joint Laboratory of Low-Carbon Chemical Engineering of Ministry of Education, Tianjin, 300350, China., Zhen S; School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University, Tianjin, 300072, China.; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China.; Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192, China.; National Industry-Education Platform of Energy Storage, Tianjin University, 135 Yaguan Road, Tianjin, 300350, China.; International Joint Laboratory of Low-Carbon Chemical Engineering of Ministry of Education, Tianjin, 300350, China., Liu W; State Key Laboratory of Fine Chemicals, Department of Chemistry, School of Chemical Engineering, Dalian University of Technology, Dalian, 116024, China., Pei C; School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University, Tianjin, 300072, China.; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China.; Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192, China.; National Industry-Education Platform of Energy Storage, Tianjin University, 135 Yaguan Road, Tianjin, 300350, China.; International Joint Laboratory of Low-Carbon Chemical Engineering of Ministry of Education, Tianjin, 300350, China.; Zhejiang Institute of Tianjin University, Ningbo, 315201, Zhejiang, China., Zhang P; School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University, Tianjin, 300072, China. p_zhang@tju.edu.cn.; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China. p_zhang@tju.edu.cn.; Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192, China. p_zhang@tju.edu.cn.; National Industry-Education Platform of Energy Storage, Tianjin University, 135 Yaguan Road, Tianjin, 300350, China. p_zhang@tju.edu.cn.; International Joint Laboratory of Low-Carbon Chemical Engineering of Ministry of Education, Tianjin, 300350, China. p_zhang@tju.edu.cn.; Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou, 350207, Fujian, China. p_zhang@tju.edu.cn., Zhao ZJ; School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University, Tianjin, 300072, China. zjzhao@tju.edu.cn.; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China. zjzhao@tju.edu.cn.; Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192, China. zjzhao@tju.edu.cn.; National Industry-Education Platform of Energy Storage, Tianjin University, 135 Yaguan Road, Tianjin, 300350, China. zjzhao@tju.edu.cn.; International Joint Laboratory of Low-Carbon Chemical Engineering of Ministry of Education, Tianjin, 300350, China. zjzhao@tju.edu.cn., Gong J; School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University, Tianjin, 300072, China. jlgong@tju.edu.cn.; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China. jlgong@tju.edu.cn.; Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192, China. jlgong@tju.edu.cn.; National Industry-Education Platform of Energy Storage, Tianjin University, 135 Yaguan Road, Tianjin, 300350, China. jlgong@tju.edu.cn.; International Joint Laboratory of Low-Carbon Chemical Engineering of Ministry of Education, Tianjin, 300350, China. jlgong@tju.edu.cn.; Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou, 350207, Fujian, China. jlgong@tju.edu.cn.; Tianjin Normal University, Tianjin, 300387, China. jlgong@tju.edu.cn. |
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Jazyk: | angličtina |
Zdroj: | Nature communications [Nat Commun] 2024 Sep 17; Vol. 15 (1), pp. 8169. Date of Electronic Publication: 2024 Sep 17. |
DOI: | 10.1038/s41467-024-52519-8 |
Abstrakt: | Low-cost, efficient catalyst high-throughput screening is crucial for future renewable energy technology. Interpretable machine learning is a powerful method for accelerating catalyst design by extracting physical meaning but faces huge challenges. This paper describes an interpretable descriptor model to unify activity and selectivity prediction for multiple electrocatalytic reactions (i.e., O (© 2024. The Author(s).) |
Databáze: | MEDLINE |
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