Recent progress in data-driven approaches for single-atom catalysts in lithium-sulfur batteries

Lithium-sulfur batteries (LSBs) have emerged as promising next-generation energy storage systems due to their high theoretical energy density and cost-effectiveness. However, their practical application is severely impeded by the sluggish sulfur redox kinetics and polysulfide shuttle effect. Single-atom catalysts (SACs), featuring atomically dispersed active sites and tunable electronic structures, offer new opportunities to address these challenges. With the advent of the fourth paradigm of data-driven scientific discovery, the design and application of SACs in LSBs have experienced rapid development and achieved remarkable success. This review systematically summarizes recent progress in SACs for LSBs, with a particular emphasis on their roles in both cathode host materials and modified separators. Special attention is devoted to insights from first-principles calculations, which elucidate the structure-activity relationships of SACs featuring diverse central metal atoms and coordination environments. Furthermore, we highlight the emerging integration of density functional theory with machine learning (ML), especially interpretable ML models for accelerated catalyst screening and deeper mechanistic understanding. Finally, current challenges and future opportunities are discussed, including the structural stability of SACs under working conditions, the development of universal activity descriptors, and the implementation of data-driven design strategies. This review aims to offer a comprehensive perspective for the data-driven design of SACs, thereby facilitating the practical application of high energy density LSBs.