Accelerated prediction of Cu-based single-atom alloy catalysts for CO_(2) reduction by machine learning

查看全文 作　　者：Dashuai [1]Wang;Runfeng [1]Cao;Shaogang [2]Hao;Chen [1]Liang;Guangyong [3]Chen;Pengfei [4]Chen;Yang [5]Li;Xiaolong [1]Zou

机构地区：[1]Shenzhen Geim Graphene Center,Tsinghua-Berkeley Shenzhen Institute&Tsinghua Shenzhen International Graduate School,Tsinghua University,Shenzhen,518055,China;[2]Tencent,Shenzhen,518054,China;[3]Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology,Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,China;[4]Department of Mechanical and Automation Engineering,The Chinese University of Hong Kong,Hong Kong,999077,China;[5]Tsinghua-Berkeley Shenzhen Institute,Tsinghua University,Shenzhen,518055,China

出　　处：《Green Energy & Environment》索引2023年第8卷第3期,共11页

基　　金：supported by the National Natural Science Foundation of China (Grant Nos.62006219 and 62001266);Guangdong Innovative and Entrepre-neurial Research Team Program (grant No.2017ZT07C341);the Bureau of Industry and Information Technology of Shenzhen for the 2017 Graphene Manufacturing Innovation Center Project (No.201901171523);the China Postdoctoral Science Foundation (No.2020M680506);Guangdong Basic and Applied Basic Research Foundation (No.2020A1515110338). 摘　　要：Various strategies,including controls of morphology,oxidation state,defect,and doping,have been developed to improve the performance of Cu-based catalysts for CO_(2) reduction reaction(CO_(2)RR),generating a large amount of data.However,a unified understanding of underlying mechanism for further optimization is still lacking.In this work,combining first-principles calculations and machine learning(ML)techniques,we elucidate critical factors influencing the catalytic properties,taking Cu-based single atom alloys(SAAs)as examples.Our method relies on high-throughput calculations of 2669 CO adsorption configurations on 43 types of Cu-based SAAs with various surfaces.Extensive ML analyses reveal that low generalized coordination numbers and valence electron number are key features to determine catalytic performance.Applying our ML model with cross-group learning scheme,we demonstrate the model generalizes well between Cu-based SAAs with different alloying elements.Further,electronic structure calculations suggest surface negative center could enhance CO adsorption by back donating electrons to antibonding orbitals of CO.Finally,several SAAs,including PCu,AgCu,GaCu,ZnCu,SnCu,GeCu,InCu,and SiCu,are identified as promising CO_(2)RR catalysts.Our work provides a paradigm for the rational design and fast screening of SAAs for various electrocatalytic reactions. 关键词：Cu-based single-atom alloy CO adsorption Machine learning First principles CO_(2)reduction reaction

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Accelerated prediction of Cu-based single-atom alloy catalysts for CO_(2) reduction by machine learning

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