Machine learning assisted binary alloy catalyst design for the electroreduction of CO2 to C2 products

Abstract

The carbon dioxide reduction reaction (CO₂RR) has become one of the most important catalytic reactions due to its potential impact on global emissions. Among the many products this reaction yields, C₂ products are the most valuable due to their potential use as hydrocarbon fuels. For the efficient conversion of CO₂ into C₂ products, however, further work needs to be done on understanding the reaction pathway mechanisms and ideal catalytic surfaces. Herein, we gain insight into the C₂ pathway through a combination of Density Functional Theory (DFT) and machine learning (ML) by studying the adsorption of *COCOH on eight different types of Cu-based binary alloy catalysts (BAC) and subsequently discover the ideal BAC surfaces through configurational space exploration. 8 different ML models were evaluated with descriptors for elemental period, group, electronegativity, and the number of unpaired d orbital electrons. The top performing models could successfully predict the adsorption energy of *COCOH on Cu-based BACs to within 0.095 eV mean absolute error (MAE). The most accurate models found Cu/Ag and Cu/Au BACs with 2–3 atom nanoislands on the surface and high Ag/Au density subsurfaces had the most favorable reaction energy pathway which corresponds with the weakest *COCOH adsorption energies.