Electrochemical CO2 reduction reaction (CO2RR) presents a green alternative to the production of energy-dense hydrocarbons, while mitigating atmospheric CO2. However, a key bottleneck in the implementation of this electrochemical process is the discovery of an efficient and selective catalyst surface. While copper has been identified as the only monometal catalyst to exhibit appreciable performance for the production of value-added products, opportunities exist to further enhance its catalytic activity and selectivity. One such strategy is the addition of supplementary adsorbates to the copper surface, which has been found to greatly influence its CO2RR activity and selectivity. However, the mechanism behind this phenomenon remains unclear and its beneficial role is scarcely explored. Considering all the potential supplementary adsorbates and their combinations, a full analysis using either experiments or density functional theory (DFT) calculations would be both costly and time-intensive. In this talk, we present an accurate and high-throughput screening strategy based on machine learning (ML) in conjunction with DFT calculations to rapidly screen hundreds of adsorbate combinations on a copper surface using simple features, with the aim of identifying combinations that would promote CO2RR performance compared to pristine Cu. We implement a rigorous feature selection process to reduce the dimensionality of our feature space, resulting in improvements in both prediction accuracy and computation time. Using the extreme gradient boosting regressor, we identified 81 and 152 unique supplementary adsorbate combinations to promote methane and C2+ production compared to pristine copper, respectively, while 80 unique combinations are shown to simultaneously improve both methane and C2+ production. Our results not only provide new insights into the role of supplementary adsorbates to guide future experimental and theoretical studies but also demonstrate the efficacy of using machine learning as an efficient and accurate screening tool for large combinatory systems in energy-related applications.

Machine learning investigation of supplementary adsorbate influence on copper for enhanced electrochemical CO2 reduction performance

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ACS Fall 2022

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