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    Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction
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    • Jiawei Zhang
      Jiawei Zhang
      Key Laboratory for Advanced Materials, Research Institute of Industrial Catalysis and Centre for Computational Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, P. R. China
      More by Jiawei Zhang
    • Peijun Hu
      Peijun Hu
      Key Laboratory for Advanced Materials, Research Institute of Industrial Catalysis and Centre for Computational Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, P. R. China
      School of Chemistry and Chemical Engineering, The Queen’s University of Belfast, Belfast, BT9 5AG, U.K.
      More by Peijun Hu
      Orcidhttp://orcid.org/0000-0002-6318-1051
    • Haifeng Wang*
      Haifeng Wang
      Key Laboratory for Advanced Materials, Research Institute of Industrial Catalysis and Centre for Computational Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, P. R. China
      *Email: [email protected]
      More by Haifeng Wang
      Orcidhttp://orcid.org/0000-0002-6138-5800
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    The Journal of Physical Chemistry C

    Cite this: J. Phys. Chem. C 2020, 124, 19, 10483–10494
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    https://doi.org/10.1021/acs.jpcc.0c00406
    Published April 22, 2020
    Copyright © 2020 American Chemical Society
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    Abstract

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    The prediction of chemisorption energy to facilitate the high-throughput screening of active catalysts has been long pursued but remains challenging. In particular, amorphous materials usually exhibit superior activity and have drawn ever-increasing attention in heterogeneous catalysis. However, the insight into the basic structure–property relation remains far from sufficient owing to their disordered structure and untracked surface state, let alone the effective prediction of adsorption energy. Here, employing the amorphous Ni2P catalyst as an example and powerful machine learning (ML) models, we propose an effective strategy that enables fast and quantitative prediction of the adsorption energy of hydrogen on amorphous Ni2P surfaces. Specifically, our method decomposes the difficult prediction of adsorption energy on amorphous surfaces into two subproblems: frozen adsorption energy and relaxation energy. By training with a set of ab initio adsorption energies within a wide configuration space, we succeed to predict the adsorption energies with ∼0.1 eV error by adopting the feature only relying on local chemical environment. Our strategy allows us to successfully implement the high-throughput exploration of active sites for hydrogen evolution reaction (HER). This work builds a predictive model of site-specific chemisorption energy, and the related statistical analysis underpins the fundamental understanding of the chemical bond, which could largely facilitate rational design of active amorphous catalysts.

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    Cite this: J. Phys. Chem. C 2020, 124, 19, 10483–10494
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jpcc.0c00406
    Published April 22, 2020
    Copyright © 2020 American Chemical Society
    Request reuse permissions

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