Investigation of dual atom doped single-layer MoS2 for electrochemical reduction of carbon dioxide by first-principle calculations and machine-learning

Abstract

The exploration of efficient electrocatalysts for carbon dioxide reduction reaction (CO2RR) with viable activity and superior selectivity remains a great challenge. The efficiency of CO2RR over traditional transition metal-based catalysts is restricted by their inherent scaling relationships, so breaking this scaling relationship is the key to improving the catalytic performance. In this work, inspired by the recent experimental progress in the synthesis of dual atom catalysts (DACs), we reported a rational design of novel DACs with two transition metal atoms embedded in defective MoS2 with S vacancies for CO2 reduction; 21 metal dimer systems were selected, including six homonuclear catalysts (MoS2-M2, M = Cu, Fe, Ni, Mn, Cr, Co) and 15 heteronuclear catalysts (MoS2-M1M2). First-principles calculations showed that the MoS2-NiCr system not only breaks the linear relationship of key intermediates but also possesses a low overpotential of 0.58 V and superior selectivity in the process of methane generation, which can be used as a promising catalyst for methane formation from CO2 electroreduction. Notably, by combining random forest regression machine learning study, we found that the CO2RR activity of DACs is essentially controlled by some fundamental factors, such as the distance between metal centers and the number of outer electrons in the metal atoms. Our findings provide profound insights for the design of efficient DACs for CO2RR.