Integrating Machine Learning and DFT Descriptors for Screening Dual Metal‐Site Catalysts for CO ₂ Reduction to C ₂ Products

The electrochemical reduction of CO₂ (CO₂RR) offers a sustainable route to generate multi-carbon products (C₂), but achieving high activity and selectivity remains a challenge. Dual metal-site catalysts (DMSCs), composed of two adjacent metal sites, provide unique active centers that enable simultaneous CO adsorption, a key step for C─C coupling. Here, we systematically investigated 156 DMSCs supported on nitrogen-doped carbon to identify promising candidates for CO₂RR. Stability screening revealed that 8 DMSCs are unstable, while hydrogen adsorption calculations excluded 6 additional systems due to strong *H binding. Among the remaining catalysts, 33 DMSCs exhibit CO dimerization energies (∆G*CO dimer-2*CO) below 0.75 eV, indicating favorable activity toward C₂ products. To explain these trends, we performed a linear correlation analysis of CO dimerization energy against 71 electronic parameters, which revealed that the occupancy of the dz²↓ orbital (denoted O-dz²↓) exhibits the highest correlation (R² = 0.69). This suggested that combinations of electronic parameters could further improve the correlation and accurately predict CO₂RR toward C₂ products. To achieve this, multiple machine learning models were trained, with the random forest regressor (RFR) achieving superior performance (R² = 0.98 for training and 0.96 for testing), demonstrating its suitability for predicting CO dimerization energy. Furthermore, the overpotential for C₂ production of the 33 DMSCs was correlated with electronic descriptors, revealing that O-dz²↓ exhibited the highest correlation (R² = 0.87), attributable to the substantial population of dz²↓ states near the Fermi level (EF), thereby underscoring its significance as a key descriptor. Overall, we emphasize the importance of using a multi-descriptor predictive model to accurately estimate CO dimerization energies, and we identify key electronic parameters of DMSCs that can predict the overpotential for C₂ products. These insights offer a valuable framework for the rapid screening of low-cost materials with high selectivity toward C₂ products.