Data-Driven Insight into the Universal Structure–Property Relationship of Catalysts in Lithium–Sulfur Batteries

作者：Zhiyuan Han, Shengyu Tao, Yeyang Jia, Mengtian Zhang, Ruifei Ma, Xiao Xiao, Jiaqi Zhou, Runhua Gao, Kai Cui, Tianshuai Wang, Xuan Zhang, Guangmin Zhou · 发表于：Journal of the American Chemical Society · 年份：2025 · DOI：10.1021/jacs.5c04960 · 被引用次数：40 · 研究领域：Advanced Battery Materials and Technologies、Advancements in Battery Materials、Advanced Battery Technologies Research

Despite tremendous efforts in catalyzing the sulfur reduction reaction (SRR) in high-capacity lithium–sulfur (Li–S) batteries, understanding the universal and quantitative structure–property relationships (UQSPRs) of SRR remains elusive. Such an unclarity results from the limitations of first-principle calculations in analyzing vast, high-dimensional, and heterogeneous data. Here, we present a collaborative data-driven model for heterogeneous catalytic knowledge fusion, detecting over 2,900 articles on SRR published between 2004 and 2024. By using sure independence screening and sparsifying operator, we surprisingly identified a composite descriptor, D, dominated by the dispersion factor. In contrast to the classical electronic state analysis framework, the dispersion factor directly established UQSPRs between atom topological arrangement and catalyst-polysulfide interaction intensity, accurately predicting the catalytic activity of over 800 types of catalysts. Combined with a volcano plot linking the overpotential to the interaction intensity, we determined the D value range of high catalytic activity, facilitating the discovery of tens of novel SRR catalysts from 374,833 candidates, many of which escaped previous human chemical intuition. As a representative, CrB 2 demonstrated superior catalytic activity under high sulfur loadings of 12.0 mg cm –2 and low temperatures of −25 °C. Pouch cells with CrB 2 achieved a gravimetric specific energy of 436 Wh kg –1 under a high sulf...