Synergizing Machine Learning with High-Throughput DFT to Design Efficient Single-Atom Catalysts for Hydrogen Evolution Reaction.

Journal: Small methods
Published Date: Aug 12, 2025

Abstract

The development of efficient single-atom catalysts (SACs) for electrocatalytic hydrogen evolution (HER) has garnered significant attention within the scientific community. However, the extensive scope of material experimentation, coupled with high research and development costs and prolonged research cycles, severely hampers the efficient advancement of related materials. In this study, the HER activity of 90 types of SACs is systematically investigated, which consist of single transition metal (TM) and/or nonmetal (NM) atoms bonded in graphyne (TM-NM-GY), by synergizing machine learning algorithms with high-throughput DFT computations. The findings reveal that the HER catalytic activity of Fe-GY, Fe-B-GY, Ni-B-GY, Pd-B-GY, Sc-N-GY, Co-N-GY, Y-N-GY, and Pd-N-GY surpasses that of commercial Pt/C catalysts. Moreover, non-metallic B or N atom doping can effectively modulate the HER performance of SACs. Furthermore, it is confirmed that HER activity correlates with characteristic factors such as the bond length of the coordinating atoms, d-band center, metal binding height, charge transfer, and ICOHP. Finally, machine learning stacking models have proven efficient in predicting and designing superior HER SACs. It is anticipated that these insights will accelerate the prediction and design of corresponding SACs.

Authors

  • Shu-Long Li
    College of Materials and Energy, Guang'an Institute of Technology, Guang'an, Sichuan, 638000, China.
  • Hongyuan Zhou
    Shanghai Zhongqiao Health Intelligent Technology Co., Ltd., Shanghai, 201203.
  • Zuhui Zhou
    Institute for Advanced Study, Chengdu University, Chengdu, 610106, China.
  • Li-Yong Gan
    College of Physics and Center of Quantum Materials and Devices, Chongqing University, Chongqing, 401331, China.
  • Fanggong Cai
    Key Laboratory of materials and surface technology (Ministry of Education), School of Materials Science and Engineering, Xihua University, Chengdu, Sichuan, 610039, China.
  • Yong Zhao
    a School of Mathematics and Information Science , Henan Polytechnic University , Jiaozuo 454000 , People's Republic of China.
  • Jianping Long
    Gansu Provincial Maternal and Child Health Care Hospital (Gansu Provincial Central Hospital), Lanzhou, China.
  • Liang Qiao
    Department of Chemistry, Shanghai Stomatological Hospital, and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China. liang_qiao@fudan.edu.cn.

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