Advancing CO2RR with O-Coordinated Single-Atom Nanozymes: A DFT and Machine Learning Exploration

Electrochemical CO₂ reduction reaction (CO₂RR) offers a promising route toward zero-carbon emissions. Recently emerged single-atom nanozymes (SANs), which combine the advantages of single-atom catalysts (SACs) and nanozymes, show potential in CO₂RR electrocatalysis applications. Herein, we designed 260 carbon-supported TMO₄ SANs, introducing heteroatoms outside the coordination sphere to modulate the interaction between the first-coordination sphere and the support structure. Using a collaborative workflow of the density functional theory (DFT) and machine learning (ML), we assessed these SANs for the CO₂RR performance. Among them, 185 SANs demonstrated high stability. Our model, based on extreme gradient boosting regression (XGBR) and trained on 100 labeled SANs, successfully predicted the limiting potentials (U_L) for 85 SANs. From these, we distinguished 33 SANs with superior activity, good specific product selectivity, and greater hydrogen evolution reaction (HER) suppression compared with benchmark TMN₄-C catalysts. Particularly, MoO₄ supported on external H-doped graphene (Mo-I) and FeO₄ supported on external N,O-doped graphene (Fe-VI) exhibited remarkable enzyme-mimicking characteristics, with Mo-I showing a formate dehydrogenase-like behavior and Fe-VI mimicking CO dehydrogenase with U_L values of −0.05 and −0.06 V, respectively. Through ML feature engineering and electronic structure property analysis, we determined the first-coordination sphere–support interaction (FCSSI) as a key descriptor in regulating the catalyst performance. We hope that these insights may provide valuable guidance for exploring potential SANs for CO₂RR.