Volume 8 Issue 3
Jul.  2023
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Article Navigation >  Green Energy&Environment  > 2023  >  8(3): 820-830
Dashuai Wang, Runfeng Cao, Shaogang Hao, Chen Liang, Guangyong Chen, Pengfei Chen, Yang Li, Xiaolong Zou. Accelerated prediction of Cu-based single-atom alloy catalysts for CO2 reduction by machine learning. Green Energy&Environment, 2023, 8(3): 820-830. doi: 10.1016/j.gee.2021.10.003
Citation: Dashuai Wang, Runfeng Cao, Shaogang Hao, Chen Liang, Guangyong Chen, Pengfei Chen, Yang Li, Xiaolong Zou. Accelerated prediction of Cu-based single-atom alloy catalysts for CO2 reduction by machine learning. Green Energy&Environment, 2023, 8(3): 820-830. doi: 10.1016/j.gee.2021.10.003
shu

Accelerated prediction of Cu-based single-atom alloy catalysts for CO2 reduction by machine learning

doi: 10.1016/j.gee.2021.10.003

  • a. Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute & Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China;

  • b. Tencent, Shenzhen, 518054, China;

  • c. Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China;

  • d. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, 999077, China;

  • e. Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, 518055, China

Funds:

This work was 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) and Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515110338). D. Wang and R. Cao contributed equally.

  • Received date 13 July 2021, Accepted date 25 October 2021, RevRecd date 28 September 2021, Publish date 31 July 2023,
    Abstract
  • Various strategies, including controls of morphology, oxidation state, defect, and doping, have been developed to improve the performance of Cu-based catalysts for CO2 reduction reaction (CO2RR), 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 CO2RR catalysts. Our work provides a paradigm for the rational design and fast screening of SAAs for various electrocatalytic reactions.

     

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