Recent Advances in Data‐Driven Design of Dual‐Atom Catalysts

Abstract Dual‐atom catalysts (DACs) have emerged as a cutting‐edge research frontier in energy and environmental catalysis. With the integration of high‐throughput density functional theory calculations and machine learning (ML) algorithms, data‐driven design strategies are reshaping traditional material discovery workflows. These approaches enable the establishment of structure–property relationships that guide the rational construction of efficient active sites in DACs. This review highlights the advantages of data‐driven methodologies in DAC design, focusing on structural optimization strategies including dual‐site configuration tuning and microcoordination environment regulation. It further outlines typical ML workflows for DAC screening and showcases representative applications in energy conversion and environmental remediation. Finally, the significance of developing a closed‐loop system that integrates theoretical calculations, intelligent prediction, and experimental validation is underscored, and perspectives for advancing this evolving research paradigm are offered.