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.
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 2 /CO 2 /N 2 reduction and O 2 evolution reactions), utilizing only easily accessible intrinsic properties. This descriptor, named ARSC, successfully decouples the atomic property (A), reactant (R), synergistic (S), and coordination effects (C) on the d-band shape of dual-atom sites, which is built upon our developed physically meaningful feature engineering and feature selection/sparsification (PFESS) method. Driven by this descriptor, we can rapidly locate optimal catalysts for various products instead of over 50,000 density functional theory calculations. The model's universality has been validated by abundant reported works and subsequent experiments, where Co-Co/Ir-Qv3 are identified as optimal bifunctional oxygen reduction and evolution electrocatalysts. This work opens the avenue for intelligent catalyst design in high-dimensional systems linked with physical insights.
(© 2024. The Author(s).)
Databáze: MEDLINE
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