Journal
JOURNAL OF PHYSICAL CHEMISTRY C
Volume 124, Issue 36, Pages 19571-19578Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpcc.0c04432
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Funding
- U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division, Catalysis Science Program
- Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]
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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 (Delta G(H)) 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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