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Lithium-sulfur (Li-S) batteries with high energy density and low cost are promising for next-generation energy storage. However, their cycling stability is plagued by the high solubility of lithium polysulfide (LiPS) intermediates, causing fast capacity decay and severe self-discharge. Exploring electrolytes with low LiPS solubility has shown promising results toward addressing these challenges. However, here, we report that electrolytes with moderate LiPS solubility are more effective for simultaneously limiting the shuttling effect and achieving good Li-S reaction kinetics. We explored a range of solubility from 37 to 1,100 mM (based on S atom, [S]) and found that a moderate solubility from 50 to 200 mM [S] performed the best. Using a series of electrolyte solvents with various degrees of fluorination, we formulated the Single-Solvent, Single-Salt, Standard Salt concentration with Moderate LiPSs solubility Electrolytes (termed S6MILE) for Li-S batteries. Among the designed electrolytes, Li-S cells using fluorinated-1,2-diethoxyethane S6MILE (F4DEE-S6MILE) showed the highest capacity of 1,160 mAh g-1 at 0.05 C at room temperature. At 60 °C, fluorinated-1,4-dimethoxybutane S6MILE (F4DMB-S6MILE) gave the highest capacity of 1,526 mAh g-1 at 0.05 C and an average CE of 99.89% for 150 cycles at 0.2 C under lean electrolyte conditions. This is a fivefold increase in cycle life compared with other conventional ether-based electrolytes. Moreover, we observed a long calendar aging life, with a capacity increase/recovery of 4.3% after resting for 30 d using F4DMB-S6MILE. Furthermore, the correlation between LiPS solubility, degree of fluorination of the electrolyte solvent, and battery performance was systematically investigated. [ABSTRACT FROM AUTHOR]

Improving Coulombic efficiency (CE) is key to the adoption of high energy density lithium metal batteries. Liquid electrolyte engineering has emerged as a promising strategy for improving the CE of lithium metal batteries, but its complexity renders the performance prediction and design of electrolytes challenging. Here, we develop machine learning (ML) models that assist and accelerate the design of high-performance electrolytes. Using the elemental composition of electrolytes as the features of our models, we apply linear regression, random forest, and bagging models to identify the critical features for predicting CE. Our models reveal that a reduction in the solvent oxygen content is critical for superior CE. We use the ML models to design electrolyte formulations with fluorine-free solvents that achieve a high CE of 99.70%. This work highlights the promise of data-driven approaches that can accelerate the design of high-performance electrolytes for lithium metal batteries. [ABSTRACT FROM AUTHOR]