4.8 Article

Design of Graphdiyne and Holey Graphyne-Based Single Atom Catalysts for CO2 Reduction With Interpretable Machine Learning

Journal

ADVANCED FUNCTIONAL MATERIALS
Volume -, Issue -, Pages -

Publisher

WILEY-V C H VERLAG GMBH
DOI: 10.1002/adfm.202213543

Keywords

density functional theory computations; electrochemical CO2 reduction reaction; graphdiyne; holey graphyne; machine learning; single-atom catalysts

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Using density functional theory computations, researchers identified graphdiyne and holey graphyne supported single-atom catalysts (SACs) as promising candidates for electrochemical CO2 reduction reaction (CO2RR). They found 25 catalysts that effectively activate CO2 and inhibit hydrogen evolution, with 8 of them showing higher activity for CH4 production than Cu(211). The study also revealed the impact of support materials on limiting potentials and reaction pathways, and machine learning was used to predict the activity of other SACs.
Using electrochemical CO2 reduction reaction (CO2RR) to synthesize value-added hydrocarbons provides a useful solution for environmental issues and energy crisis. However, this process is impeded by the low activity and selectivity of electrocatalysts toward targeted products. Employing density functional theory computations, the graphdiyne and holey graphyne supported single-atom catalysts (SACs, M/GDY and M/HGY) are demonstrated to be the promising candidates for the CO2RR. By taking full elemental diversity of metal sites across the periodic table, 25 catalysts are found to effectively activate CO2 and inhibit competitive hydrogen evolution, and 8 of them show higher activity for CH4 production than Cu(211). Remarkably, changing supports are found to greatly affect limiting potentials and reaction pathways, even leading to an inert-active transition for some metal centers. The resulting SACs, including Mn/GDY, Co/HGY, Ru/GDY, and Os/GDY, can achieve high activity with low limiting potentials of & AP; -0.22 to -0.58 V. Machine learning enables to identify the critical role of the polarized charge and magnetic moment of metal atoms in affecting the activity. The built machine learning model also shows an interpretable capability to predict the activity of the other types of SACs, offering a great promise to quick screening of high-performance SACs.

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