Liu Xinyan, Peng Hong-Jie
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China.
Patterns (N Y). 2025 May 9;6(5):101256. doi: 10.1016/j.patter.2025.101256.
The exploration of efficient catalysts for sluggish sulfur redox reactions is pivotal for advancing lithium-sulfur batteries but remains inefficient through trial-and-error approaches. In a recent study, Zhou, Li, and colleagues proposed an explainable-AI-based approach to intelligently design catalysts adaptive to diverse local chemical environments in batteries, achieving exceptional catalytic and battery performance.
探索用于缓慢硫氧化还原反应的高效催化剂对于推进锂硫电池至关重要，但通过试错法仍效率低下。在最近的一项研究中，周、李及其同事提出了一种基于可解释人工智能的方法，以智能设计适用于电池中不同局部化学环境的催化剂，实现了卓越的催化和电池性能。
