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

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, CrB₂ demonstrated superior catalytic activity under high sulfur loadings of 12.0 mg cm^–2 and low temperatures of −25 °C. Pouch cells with CrB₂ achieved a gravimetric specific energy of 436 Wh kg^–1 under a high sulfur content of 76.1% and lean-electrolyte conditions of 2.8 μL mg^–1. Our data-driven method enables new opportunities to fundamentally identify UQSPRs using vast and heterogeneous data, suggesting the promise of revisiting under-exploited knowledge from the historical literature for novel catalyst discovery.