Accelerated Screening of Dual-Atom-Doped Copper Electrocatalysts for CO ₂ -to-C ₂₊ Conversion via a Density Functional Theory-Guided Ten-Feature Interpretable Machine Learning Strategy

Electrochemical CO 2 reduction (ECO 2 RR) to multi-carbon products (C 2+ ) offers a promising approach to mitigate carbon emissions; however, the rational design of Cu electrocatalysts with high selectivity and low overpotential remains challenging and suffers from time-consuming trial-and-error experiments. This study introduces a density functional theory (DFT) and machine learning (ML) combined strategy to discover dual-atom-doped Cu electrocatalysts (A-B@Cu) for selective C 2+ production. DFT calculations on the favored OC–COH dimerization reveal that doping induces lattice strain and regulates charge transfer from Cu to the OC–COH intermediate. With the A-B@Cu dataset expanded from 21 for DFT to 213 for ML, a gradient boosting regression (GBR) model, incorporating 10 features related to lattice strain and charge transfer, was developed to accurately predict the free energy of OC–COH dimerization (Δ G OC–COH ). Moreover, three of the 10 features, Charge (the number of charge transfer), Φ (an atomic property derived from SISSO), and S F,ads (Fermi softness of Cu at adsorption sites), enable the rapid prediction of Δ G OC–COH with reduced accuracy requirements. This study developed a multi-feature, theoretically robust, computationally efficient ML strategy to expedite the identification of A-B@Cu electrocatalysts for enhanced C 2+ production in ECO 2 RR.