Machine-Learning-Assisted First-Principles Approach for the Electrochemical Reduction of CO2 on Single-Atom Catalysts

Abstract

Molecular design of an active electrocatalyst can determine the primary product. Precise selection of the active metal center influences the adsorption of reactants, intermediates, and product selectivity and ultimately affects the efficiency of the process. Here, we predict a potent catalyst, namely, Fe@C6N6, with enhanced activity and low overpotential for CO2 reduction reactions (CO2RRs) utilizing the density functional theory (DFT) approach, coupled with machine-learning-based model building. A series of 24 transition-metal single-atom catalysts (SACs) anchored on the C6N6 monolayer were screened, among which 19 exhibit effective CO2 activation. Employing density functional theory (DFT), a systematic study on reaction pathways demonstrates that 13 catalysts favor CH4 formation, while the remaining 6 exhibit selectivity toward CH3OH production. Additionally, 9 among the 19 catalysts are further screened out as they preferentially promote the competing hydrogen evolution reaction (HER). The active SACs demonstrate strong CO2RR activity, with Fe@C6N6 emerging as the most promising catalyst with the lowest limiting potential (0.39 V). To further establish atomic–property relationships, a machine-learning (ML) regression model was developed to predict adsorption energies using statistically significant descriptors and identify key factors governing CO2 adsorption. Furthermore, a classification model developed using the elemental characteristics of the metal atoms shows excellent correlation with the DFT results across the dataset. This combined DFT and machine-learning approach provides mechanistic insights for the rational development of efficient SACs for CO2 electroreduction.