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The electrochemical carbon dioxide reduction reaction (CO2RR) is an attractive approach for mitigating CO2 emissions and generating value-added products. Consequently, discovery of promising CO2RR catalysts has become a crucial task, and machine learning (ML) has been utilized to accelerate catalyst discovery. However, current ML approaches are limited to exploring narrow chemical spaces and provide only fragmentary catalytic activity, even though CO2RR produces various chemicals. Here, by merging pre-developed ML model and a CO2RR selectivity map, we establish high-throughput virtual screening strategy to suggest active and selective catalysts for CO2RR without being limited to a database. Further, this strategy can provide guidance on stoichiometry and morphology of the catalyst to researchers. We predict the activity and selectivity of 465 metallic catalysts toward four expected reaction products. During this process, we discover previously unreported and promising behavior of Cu-Ga and Cu-Pd alloys. These findings are then validated through experimental methods. Conventional ab initio calculations and machine learning provide limited information on catalytic activity and selectivity and often show discrepancy with experimental results. Here, the authors report a high-throughput virtual screening strategy to identify active and selective catalysts, leading to the discovery of Cu-Ga and Cu-Pd catalysts for CO2 electroreduction. [ABSTRACT FROM AUTHOR]

Electrocatalysis, lithium-sulfur batteries, local coordination environment engineering, M-N-C catalysts, single-atom catalysts Atomic Structure Modification of Fe-N-C Catalysts via Morphology Engineering of Graphene for Enhanced Conversion Kinetics of Lithium-Sulfur Batteries (Adv. Funct. Keywords: electrocatalysis; lithium-sulfur batteries; local coordination environment engineering; M-N-C catalysts; single-atom catalysts EN electrocatalysis lithium-sulfur batteries local coordination environment engineering M-N-C catalysts single-atom catalysts 1 1 1 05/11/22 20220509 NES 220509 B Multi-Metal Oxide Nanoparticles b Multi-metal oxide (MMO) nanomaterials have significant potential to facilitate various demanding (electro)catalytic reactions, but its intrinsic complexity hinders the in-depth understanding of the origin of the catalytic activity. [Extracted from the article]