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September 10, 2025Chemical Society Reviews58 citationsOpen Access

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

ZYZiqing YaoNational University of Defense Technology YZY. ZouNational University of Defense Technology SLShuangke LiuNational University of Defense Technology

Key Points

The research identifies critical reactivity descriptors that influence the sulfur reduction reaction kinetics in lithium–sulfur batteries.
A new approach for catalyst design is proposed, integrating machine learning to improve the efficiency of sulfur reduction reactions.
Insights into the mechanistic behavior of catalysts are provided, which can lead to higher activity catalysts for lithium-sulfur batteries.
The study emphasizes the potential of leveraging machine learning techniques to optimize catalyst performance and reduce reaction barriers.

Abstract

This work elucidates the intrinsic mechanism of catalysts to enhance the kinetics of sulfur reduction reaction from a descriptor perspective and proposes a new paradigm for the development of high activity catalysts incorporating machine learning.

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