Integrating Active Learning and DFT for Fast-Tracking Single-Atom Alloy Catalysts in CO-to-Fuel Conversion.

Electrocatalytic carbon dioxide reduction (CORR) technology enables the conversion of excessive CO into high-value fuels and chemicals, thereby mitigating atmospheric CO concentrations and addressing energy scarcity. Single-atom alloys (SAAs) possess the potential to enhance the CORR performance by full utilization of atoms and breaking linear scaling relationships. However, quickly screening high-performance metal portfolios of SAAs remains a formidable challenge. In this study, we proposed an active learning (AL) framework to screen high-performance catalysts for CORR to yield fuels such as CH and CHOH. After four rounds of AL iterations, the ML model attained optimal prediction performance with the test set of approximately 0.94 and successful prediction was achieved for the binding free energy of *CHO, *COH, *CO, and *H on 380 catalyst surfaces with an accuracy within 0.20 eV. Subsequent analysis of the SAA catalysts' activity, selectivity, and stability led to the discovery of eight previously unexplored SAA catalysts for CORR. Notably, the surface activity of Ti@Cu(100), Au@Pt(100), and Ag@Pt(100) shines prominently. Utilizing DFT calculations, we elucidated the complete reaction pathway of the CORR on the surfaces of these catalysts, confirming their high catalytic activity with limiting potentials of -0.11, -0.34, and -0.46 eV, respectively, which are significantly lower than those of pure Cu catalysts. The results showcase the exceptional predictive prowess of AL, providing a valuable reference for the design of CORR catalysts.