Data-Driven Discovery of Single-Atom Catalysts for CO₂ Reduction Considering the pH-Dependency at the Reversible Hydrogen Electrode Scale

The electrochemical carbon dioxide reduction reaction (CO₂RR) represents a promising approach to mitigating climate change and addressing energy challenges by converting CO₂ into value-added chemicals. Among various CO₂RR products, CO is attractive due to its economic viability and industrial relevance. By integrating large-scale data mining (with 939 experimental performance data), we reveal that the catalytic performance of d-block transition metal-based single-atom catalysts (SACs) for CO₂RR is influenced not only by the coordination environment but also significantly by pH. However, the unified model that could accurately depict the pH-dependent CO₂RR to CO activity of d-block SACs is urgently needed. Herein, we conducted pH-dependent microkinetic modeling based upon density functional theory calculations and pH-electric field coupled microkinetic modeling to analyze CO₂RR performance of 101 SACs. Our data-driven screening identifies 12 high-performance SACs with promising CO selectivity across different pH conditions, primarily based on Fe, Cu, and Ni centers. We establish scaling relation between key intermediates (*COOH and *CO) and analyze their adsorption behaviors under varying electrochemical conditions. Furthermore, our pH-dependent microkinetic modeling reveals the critical role of electric field effects in determining catalytic performance, aligning well with experimental TOF values. Most importantly, our theoretical model accurately captures the pH-dependent performance of CO₂RR-to-CO on d-block SACs, which is experimentally validated and serves as a general theoretical framework for the rational design of high-performance CO₂RR catalysts. Based on this model, we identify a series of promising M-N-C catalysts, providing a universal design principle for optimizing CO₂ to CO conversion.