Data-driven design of dual-metal-site catalysts for the electrochemical carbon dioxide reduction reaction

Abstract

Electroreduction of carbon dioxide (CO₂) offers a sustainable approach to realize carbon recycling and energy regeneration, and development of high-efficiency electrocatalysts for the CO₂ reduction reaction (CO₂RR) is the key scientific issue. Presently, dual-metal-site catalysts (DMSCs) have shown large potential in the electrochemical CO₂RR; however, regulating the combination and structure of many diverse transition metals is a huge challenge. Herein, we created a rational machine-learning (ML) approach to investigate the reaction activity and selectivity of 1218 DMSCs toward CO₂ electrochemical reduction. The gradient boosting regression (GBR) model possessing 17 features exhibited the best prediction accuracy with a root-mean-square error of 0.09 V and a coefficient of determination value of 0.98. By implementing two rounds of rigorous feature selection process, the screening model successfully predicted 4 DMSCs (Mn–Ru, Mn–Os, Zn–Ru and Co–Au–N₆-Gra-model 3) identified as efficient CO₂RR electrocatalysts, and then these data of ML prediction were verified by density functional theory (DFT) calculations with high accuracy (less than 0.07 V error). This work demonstrates the immense potential of ML methods and provides an efficient and accurate screening approach for the rational design of high-performance electrocatalysts.