Machine-Learning assisted screening of double metal catalysts for CO2 electroreduction to CH4

Abstract

Electrochemical CO2 reduction reaction (CO2RR) has become a promising application in addressing energy challenges and environmental crises. However, the scaling relationship between the reaction intermediates constrains the successful deep reduction of CO2. Dual-metal-site catalysts (DMSCs) have emerged as potential electrocatalysts for CO2RR by breaking the scaling relationship due to their more adaptable active sites. Herein, this study aims to investigate the correlation between the adsorption energies of essential intermediates in CO2RR catalysis with double transition metal atoms anchored on graphdiyne monolayer (TM1-TM2@GDY) through machine-learning (ML) assisted density functional theory (DFT) calculations. The results reveal the important descriptors of CO2RR catalyzed by TM1-TM2@GDY, and demonstrate that the heteronuclear TM1-TM2@GDY have great potential for deep CO2 reduction. Especially, Co-Mo@GDY and Co-W@GDY show low limiting potential (-0.60 V and −0.39 V, respectively) and high selectivity on the reaction from CO2 to CH4 based on the free energy diagrams. This study indicates that the two TM atoms on GDY act cooperatively for the catalysis of CO2RR. Notably, utilizing ML eliminates the need to calculate all transition metal combinations by DFT, which is a great boost in quickly investigating catalytic performance and high screening for excellent catalysts.