Screening of single-atom catalysts for CO2 electroreduction to CH4 using DFT calculations and machine learning

The development of highly active and selective electrocatalysts for the CO2 reduction reaction (CO2RR) is critical for renewable energy applications, particularly in the sustainable synthesis of fuels such as methane (CH4). Single-atom catalysts (SACs) have emerged as promising candidates for the CO2RR, yet the lack of efficient predictive methods hinders their systematic exploration. Herein, we combined density functional theory (DFT) calculations with machine learning (ML) to investigate a series of 3d and 4d transition metal atoms supported on C5N substrates with five different defect types as potential CO2RR catalysts. Through a rigorous five-step screening strategy, we identify nine stable TM@C5N SACs that exhibit superior catalytic activity and selectivity compared to the conventional Cu (211). Notably, our screening protocol incorporates *OH reduction, an often-overlooked factor, thereby enhancing the prediction accuracy. Among the candidates, Pd@C5N_C2 demonstrates exceptional performance, achieving a remarkably low limiting potential of 0.42 V. For ML-assisted analysis, we trained XGBoost and Random Forest models using features derived from the thermodynamic, electronic, and geometric properties. Feature importance analysis highlights intrinsic catalyst parameters, such as the d-electron (dn), first ionization energy (IE1), d-band center (epsilon d), and atomic radius (r), as dominant factors governing the CO2RR performance. By elucidating atomic-level reaction mechanisms and advancing a high-throughput screening framework, this study establishes key structure-activity relationships through ML interpretability, offering valuable insights for the rational design and accelerated discovery of high-performance SACs for the CO2RR.