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Descriptors for Hydrogen Evolution on Single Atom Catalysts in Nitrogen-Doped Graphene

  • Victor Fung
    Victor Fung
    Department of Chemistry, University of California, Riverside, California 92521, United States
    Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
    More by Victor Fung
    Orcidhttp://orcid.org/0000-0002-3347-6983
  • , 
  • Guoxiang Hu
    Guoxiang Hu
    Department of Chemistry, University of California, Riverside, California 92521, United States
    Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
    More by Guoxiang Hu
    Orcidhttp://orcid.org/0000-0003-2942-8564
  • , 
  • Zili Wu
    Zili Wu
    Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
    Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
    More by Zili Wu
    Orcidhttp://orcid.org/0000-0002-4468-3240
  • , and 
  • De-en Jiang*
    De-en Jiang
    Department of Chemistry, University of California, Riverside, California 92521, United States
    *Email: [email protected]
    More by De-en Jiang
    Orcidhttp://orcid.org/0000-0001-5167-0731
Cite this: J. Phys. Chem. C 2020, 124, 36, 19571–19578
Publication Date (Web):August 12, 2020
https://doi.org/10.1021/acs.jpcc.0c04432
Copyright © 2020 American Chemical Society
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    Abstract

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    Single-atom catalysts (SACs) are a new research frontier in electrocatalysis such as in the hydrogen evolution reaction (HER). Recent theoretical and experimental studies have demonstrated that certain M–N–C (metal–nitrogen–carbon) based SACs exhibit excellent performance for HER. Here we report a new approach to tune HER activity for SACs by changing the size and dimensionality of the carbon substrate while maintaining the same coordination environment. We screen the 3d, 4d, and 5d transition metal SACs in N-doped 2D graphene and nanographenes of several sizes for HER using first-principles density functional theory (DFT). Nanographenes containing V, Rh, and Ir are predicted to have significantly enhanced HER activity compared to their 2D graphene counterparts. We turn to machine learning to accurately predict the free energy of hydrogen adsorption (ΔGH) based on various descriptors and compressed sensing to identify key descriptors for activity, which can be used to further screen for additional candidates.

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    • Values for various descriptors of the single-atom catalysts examined in the text (PDF)

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