Data-driven discovery of electrocatalysts for CO reduction using active motifs-based machine learning.

The electrochemical carbon dioxide reduction reaction (CORR) is an attractive approach for mitigating CO emissions and generating value-added products. Consequently, discovery of promising CORR 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 CORR produces various chemicals. Here, by merging pre-developed ML model and a CORR selectivity map, we establish high-throughput virtual screening strategy to suggest active and selective catalysts for CORR 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.