Design of dual-atom catalysts for CO₂ reduction to C₂ products: From synergistic mechanisms to machine learning guidance

Single-atom catalysts (SACs) have garnered significant attention due to their maximized atomic utilization and intrinsic catalytic activity. However, their application—particularly in multi-electron pathways—is often constrained by low metal loading and the geometric isolation of active sites. In contrast, dual-atom catalysts (DACs) offer a compelling solution by enhancing site density and fostering neighboring synergistic effects. These proximal sites provide a versatile platform for breaking scaling relations, rendering DACs an ideal strategy for facilitating C-C coupling. Here, we comprehensively review the design and application of DACs for electrochemical CO₂ reduction (CO₂RR) to C₂ products. We first elucidate the reaction mechanisms, contrasting the “single-site dilemma” of SACs with the synergistic advantages of DACs. Furthermore, we examine the critical role of machine learning (ML) in accelerating catalyst discovery, highlighting its capability to efficiently navigate the vast compositional space for rational design. Finally, we summarize existing challenges and outline future directions for bridging the gap between laboratory-scale research and industrial applications.