首页|Data-Driven Design of Single-Atom Electrocatalysts with Intrinsic Descriptors for Carbon Dioxide Reduction Reaction

Data-Driven Design of Single-Atom Electrocatalysts with Intrinsic Descriptors for Carbon Dioxide Reduction Reaction

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The strategic manipulation of the interaction between a central metal atom and its coordinating environment in single-atom catalysts(SACs)is crucial for catalyzing the CO2 reduction reaction(CO2RR).However,it remains a major challenge.While density-functional theory calculations serve as a powerful tool for catalyst screening,their time-consuming nature poses limitations.This paper presents a machine learning(ML)model based on easily accessible intrinsic descriptors to enable rapid,cost-effective,and high-throughput screening of efficient SACs in complex systems.Our ML model comprehensively captures the influences of interactions between 3 and 5d metal centers and 8 C,N-based coordination environments on CO2RR activity and selectivity.We reveal the electronic origin of the different activity trends observed in early and late transition metals during coordination with N atoms.The extreme gradient boosting regression model shows optimal performance in predicting binding energy and limiting potential for both HCOOH and CO production.We confirm that the product of the electronegativity and the valence electron number of metals,the radius of metals,and the average electronegativity of neighboring coordination atoms are the critical intrinsic factors determining CO2RR activity.Our developed ML models successfully predict several high-performance SACs beyond the existing database,demonstrating their potential applicability to other systems.This work provides insights into the low-cost and rational design of high-performance SACs.

Density functional theoryMachine learningCO2 reduction reactionElectrocatalystsHigh-throughput screening

Xiaoyun Lin、Shiyu Zhen、Xiaohui Wang、Lyudmila V.Moskaleva、Peng Zhang、Zhi-Jian Zhao、Jinlong Gong

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Key Laboratory for Green Chemical Technology of Ministry of Education,School of Chemical Engineering and Technology,Tianjin University,Tianjin 300072,China

Collaborative Innovation Center of Chemical Science and Engineering(Tianjin),Tianjin 300072,China

School of Economics and Management,Tianjin University of Technology and Education,Tianjin 300222,China

Department of Chemistry,University of the Free State,P.O.Box 339,Bloemfontein 9301,South Africa

Haihe Laboratory of Sustainable Chemical Transformations,Tianjin 300192,China

National Industry-Education Platform of Energy Storage,Tianjin University,Tianjin 300350,China

Joint School of National University of Singapore and Tianjin University,International

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2024

10.1007/s12209-024-00413-1

天津大学学报(英文版)
天津大学

天津大学学报(英文版)

EI
影响因子：0.343
ISSN：1006-4982
年,卷(期)：2024.30(5)