Machine-learning-accelerated screening of multi-element doped CuSb catalysts for enhanced C2+ selectivity in CO2 electroreduction

Electrochemical CO₂ reduction (CO₂RR) to value-added fuels and chemicals offers a promising route toward carbon neutrality. However, developing efficient and selective catalysts for the generation of multi-carbon (C₂₊) products remains a significant challenge. In this work, we propose a combined density functional theory (DFT) and machine learning (ML) approach to systematically screen CuSb-based catalysts with varying surface Sb atomic fractions and non-metal dopants (O, N, S, Se, and P) on the Cu₂Sb(100) surface for CO₂RR. Approximately 200 representative adsorption configurations were randomly selected for DFT calculations, which were then used to train a predictive ML model. This model enables high-accuracy predictions of the adsorption energies of key intermediates (*CO and *H) for the remaining uncalculated configurations. By integrating the K-means clustering analysis and the optimal adsorption energy selection criteria based on the Sabatier principle, the candidate configuration with the best potential for C₂₊ product formation was identified: O-doped CuSb with a surface Sb atomic fraction of 3/12. Mechanistic studies further reveal that O doping significantly strengthens *CO adsorption while suppressing *H adsorption by modulating the electronic structure, thereby lowering the CO₂RR energy barrier and improving the thermodynamic selectivity toward C₂₊ products. This work not only elucidates the synergistic effect of surface Sb atomic fraction and non-metal dopants on CO₂RR activity, but also establishes a scalable ML prediction and screening framework, providing theoretical support and methodological pathways for the design of high-performance CuSb-based catalysts.