Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution

The electrochemical reduction of CO2 and H2 evolution from water can be used to store renewable energy that is produced intermittently. Scale-up of these reactions requires the discovery of effective electrocatalysts, but the electrocatalyst search space is too large to explore exhaustively. Here we present a theoretical, fully automated screening method that uses a combination of machine learning and optimization to guide density functional theory calculations, which are then used to predict electrocatalyst performance. We demonstrate the feasibility of this method by screening various alloys of 31 different elements, and thereby perform a screening that encompasses 50% of the d-block elements and 33% of the p-block elements. This method has thus far identified 131 candidate surfaces across 54 alloys for CO2 reduction and 258 surfaces across 102 alloys for H2 evolution. We use qualitative analyses to prioritize the top candidates for experimental validation. The design of new catalysts for electrochemical energy storage is of utmost importance. Here, an automated computational screening method is used to identify over 100 intermetallic surfaces as efficient electrocatalysts for CO2 reduction and H2 evolution.