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Machine-Learning-Driven High-Entropy Alloy Catalyst Discovery to Circumvent the Scaling Relation for CO2 Reduction Reaction

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    Research Article

    Machine-Learning-Driven High-Entropy Alloy Catalyst Discovery to Circumvent the Scaling Relation for CO2 Reduction Reaction
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    • Zhi Wen Chen
      Zhi Wen Chen
      Department of Materials Science and Engineering, University of Toronto, 184 College Street, Suite 140, Toronto, Ontario M5S 3E4, Canada
      More by Zhi Wen Chen
    • Zachary Gariepy
      Zachary Gariepy
      Department of Materials Science and Engineering, University of Toronto, 184 College Street, Suite 140, Toronto, Ontario M5S 3E4, Canada
      More by Zachary Gariepy
    • Lixin Chen
      Lixin Chen
      Department of Materials Science and Engineering, University of Toronto, 184 College Street, Suite 140, Toronto, Ontario M5S 3E4, Canada
      More by Lixin Chen
    • Xue Yao
      Xue Yao
      Department of Materials Science and Engineering, University of Toronto, 184 College Street, Suite 140, Toronto, Ontario M5S 3E4, Canada
      More by Xue Yao
    • Abu Anand
      Abu Anand
      Department of Materials Science and Engineering, University of Toronto, 184 College Street, Suite 140, Toronto, Ontario M5S 3E4, Canada
      More by Abu Anand
    • Szu-Jia Liu
      Szu-Jia Liu
      Department of Materials Science and Engineering, University of Toronto, 184 College Street, Suite 140, Toronto, Ontario M5S 3E4, Canada
      More by Szu-Jia Liu
    • Conrard Giresse Tetsassi Feugmo
      Conrard Giresse Tetsassi Feugmo
      National Research Council of Canada, Ottawa, Ontario K1A 0R6, Canada
      More by Conrard Giresse Tetsassi Feugmo
      Orcidhttps://orcid.org/0000-0002-8992-4335
    • Isaac Tamblyn
      Isaac Tamblyn
      Department of Physics, University of Ottawa, Vector Institute for Artificial Intelligence, Ottawa, Ontario K1N 6N5, Canada
      More by Isaac Tamblyn
      Orcidhttps://orcid.org/0000-0002-8146-6667
    • Chandra Veer Singh*
      Chandra Veer Singh
      Department of Materials Science and Engineering, University of Toronto, 184 College Street, Suite 140, Toronto, Ontario M5S 3E4, Canada
      Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario M5S 3G8, Canada
      *Email: [email protected]
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      Orcidhttps://orcid.org/0000-0002-6644-0178
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    ACS Catalysis

    Cite this: ACS Catal. 2022, 12, 24, 14864–14871
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    https://doi.org/10.1021/acscatal.2c03675
    Published November 22, 2022
    Copyright © 2022 American Chemical Society
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    Abstract

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    To achieve an equitable energy transition toward net-zero 2050 goals, the electrochemical reduction of CO2 (CO2RR) to chemical feedstocks through utilizing both CO2 and renewable energy is particularly attractive. However, the catalytic activity of CO2RR is limited by the scaling relation of the adsorption energies of intermediates. Circumventing the scaling relation is a potential strategy to achieve a breakthrough in catalytic activity. Herein, based on density functional theory (DFT) calculations, we designed a high-entropy alloy (HEA) system of FeCoNiCuMo with high catalytic activity for CO2RR. Machine learning models were developed by considering 1280 adsorption sites to predict the adsorption energies of COOH*, CO*, and CHO*. The scaling relation between the adsorption energies of COOH*, CO*, and CHO* is circumvented by the rotation of COOH* and CHO* on the designed HEA surface, resulting in the outstanding catalytic activity of CO2RR with the limiting potential of 0.29–0.51 V. This work not only accelerates the development of HEA catalysts but also provides an effective strategy to circumvent the scaling relation.

    Copyright © 2022 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acscatal.2c03675.

    • Details in DFT calculation, NN models, and ML feature engineering; 20 designed (111) surface structures; distributions of the adsorption energy of COOH*, CO*, and CHO* on HEA surfaces; the performance of NN models (80/20, 50/50% training–testing data splitting); Pearson correlation from NN models; reaction process of CO2RR on AS1–3; and comparison with other systems (PDF)

    • Data set including features for NN models (xlsx)

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    ACS Catalysis

    Cite this: ACS Catal. 2022, 12, 24, 14864–14871
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acscatal.2c03675
    Published November 22, 2022
    Copyright © 2022 American Chemical Society
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