Data-driven discovery of electrocatalysts for CO₂ reduction using active motifs-based machine learning.

ABSTRACT: 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

SUBMITTER: Mok DH 

PROVIDER: S-EPMC10640609 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature