Advancing Towards Intelligent and Scalable Atomic Catalysts: Insights and Prospects

Amid growing global energy and environmental pressures, the efficient conversion of CO2 into high-value chemicals has become a central challenge in catalysis research. This Perspective focuses on atomic-level structural regulation in single-and dual-atom catalysts (SACs and DACs), integrating computational chemistry with machine learning to explore emerging directions in electrocatalytic CO2 reduction (CO2RR) and the prospects for achieving highly selective C2+ product formation. We highlight the unique potential of multi-center cooperation in overcoming the intrinsic limitations of isolated single sites, particularly for C-C coupling. Building on these advances, we propose an intelligent closed-loop design strategy that integrates high-throughput computation, operando characterization, machine learning, and uncertainty quantification into a continuous feedback system. his emerging framework provides a forward-looking perspective on predictive and scalable catalyst design for achieving C2+ products in CO2 electroreduction.