Data–driven screening of electrolyte additives with high donor numbers for lithium–sulfur batteries

Electrolyte additives with high electron-donating capability are critical for regulating Li + solvation and polysulfide conversion kinetics in lithium–sulfur (Li–S) batteries. However, limited experimental Gutmann donor numbers (DNs) hinder rational additive discovery. Herein, a FrontierChargeFusion (FCF)–guided additive screening framework (FASF) is developed as a data–driven strategy that constructs the FCF by combining density functional theory (DFT)–derived HOMO, LUMO, q–, and q+ features. The FCF descriptor correlates strongly with experimental DNs (Pearson r = 0.77), reliably reflecting electron–donating ability. By integrating FCF with LUMO, Gap, and cost criteria, 38,727 pyridyl derivatives were screened, identifying 4–dimethylaminopyridine (DMAP) as the optimal candidate. Experimental and theoretical analyses demonstrate that DMAP, with its strong electron-donating ability and high lithium affinity, enhances sulfur utilization and suppresses polysulfide shuttling, yielding 807 mAh g –1 at 4 C and stable cycling in a 2.75 Ah pouch battery under lean electrolyte conditions. This work provides a scalable paradigm for data–driven additive design in advanced Li–S batteries. • Machine learning guides the construction of a novel electron donor descriptor. • A FontierChargeFusion–based screening framework accelerates the discovery of high Gutmann donor number additives. • Modulating the solvation structure of lithium polysulfides effectively enhances sulfur utilization. • A Li–S pouch cell achieves a capacity of 2.75 Ah under lean electrolyte conditions.