Reactivity descriptors for sulfur redox kinetics in lithium–sulfur batteries: from mechanistic insights to machine learning driven catalyst design

Abstract

The judicious selection of catalytic materials has emerged as a critical strategy for addressing the notorious lithium polysulfide (LiPS) shuttle effect and sluggish sulfur reduction reaction (SRR) kinetics in lithium sulfur batteries (LSBs). While traditional catalyst development has relied heavily on empirical trial-and-error approaches, recent advances in reactivity descriptor theory offer the potential to understand the mechanisms inherent in the SRR and to revolutionize the catalyst development paradigm, but a comprehensive understanding of the role and origins of descriptors in the SRR remains lacking. This review systematically examines validated descriptor-based research paradigms and their significant advances in LSBs. Firstly, we elucidate critical LiPS intermediates and rate-limiting steps in the SRR process, and present a summary of the role played by descriptors, establishing fundamental connections to descriptor functionality. Subsequently, we delineate the operational principles of three primary descriptor categories (electronic, structural, and energy descriptors) and the establishment of scaling relationships based on them. Moreover, advanced descriptor constructs are also explored, including comprehensive descriptors with multi-factor integration and other types of descriptors. In particular, we summarize how emerging artificial intelligence (AI) methodologies can facilitate the further development and application of descriptors. Ultimately, we envision great potential for clarifying the scope of applicability, developing universal descriptors, integrating with AI, and breaking the scaling relationships to accurately identify and design highly active catalysts.