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Machine learning-assisted dual-atom sites design with interpretable descriptors unifying electrocatalytic reactions

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  • Xiaoyun Lin

    (Tianjin University
    Collaborative Innovation Center of Chemical Science and Engineering (Tianjin)
    Haihe Laboratory of Sustainable Chemical Transformations
    National Industry-Education Platform of Energy Storage, Tianjin University)

  • Xiaowei Du

    (Tianjin University
    Collaborative Innovation Center of Chemical Science and Engineering (Tianjin)
    Haihe Laboratory of Sustainable Chemical Transformations
    National Industry-Education Platform of Energy Storage, Tianjin University)

  • Shican Wu

    (Tianjin University
    Collaborative Innovation Center of Chemical Science and Engineering (Tianjin)
    Haihe Laboratory of Sustainable Chemical Transformations
    National Industry-Education Platform of Energy Storage, Tianjin University)

  • Shiyu Zhen

    (Tianjin University
    Collaborative Innovation Center of Chemical Science and Engineering (Tianjin)
    Haihe Laboratory of Sustainable Chemical Transformations
    National Industry-Education Platform of Energy Storage, Tianjin University)

  • Wei Liu

    (Dalian University of Technology)

  • Chunlei Pei

    (Tianjin University
    Collaborative Innovation Center of Chemical Science and Engineering (Tianjin)
    Haihe Laboratory of Sustainable Chemical Transformations
    National Industry-Education Platform of Energy Storage, Tianjin University)

  • Peng Zhang

    (Tianjin University
    Collaborative Innovation Center of Chemical Science and Engineering (Tianjin)
    Haihe Laboratory of Sustainable Chemical Transformations
    National Industry-Education Platform of Energy Storage, Tianjin University)

  • Zhi-Jian Zhao

    (Tianjin University
    Collaborative Innovation Center of Chemical Science and Engineering (Tianjin)
    Haihe Laboratory of Sustainable Chemical Transformations
    National Industry-Education Platform of Energy Storage, Tianjin University)

  • Jinlong Gong

    (Tianjin University
    Collaborative Innovation Center of Chemical Science and Engineering (Tianjin)
    Haihe Laboratory of Sustainable Chemical Transformations
    National Industry-Education Platform of Energy Storage, Tianjin University)

Abstract

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., O2/CO2/N2 reduction and O2 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.

Suggested Citation

  • Xiaoyun Lin & Xiaowei Du & Shican Wu & Shiyu Zhen & Wei Liu & Chunlei Pei & Peng Zhang & Zhi-Jian Zhao & Jinlong Gong, 2024. "Machine learning-assisted dual-atom sites design with interpretable descriptors unifying electrocatalytic reactions," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52519-8
    DOI: 10.1038/s41467-024-52519-8
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    3. Cong Fang & Jian Zhou & Lili Zhang & Wenchao Wan & Yuxiao Ding & Xiaoyan Sun, 2023. "Synergy of dual-atom catalysts deviated from the scaling relationship for oxygen evolution reaction," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    4. Baicheng Weng & Zhilong Song & Rilong Zhu & Qingyu Yan & Qingde Sun & Corey G. Grice & Yanfa Yan & Wan-Jian Yin, 2020. "Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
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