Microscopic solvation dynamics and transport in LiFSA-Sulfone electrolytes via optimized force fields: a classical MD perspective

Abstract

Lithium bis(fluorosulfonyl)amide (LiFSA) is a commonly used lithium salt in electrolyte formulations due to its electrochemical stability, favorable ionic dissociation, and potential for enhancing lithium-ion transport in energy storage applications. Understanding the solvation dynamics and transport properties of LiFSA, particularly in mixtures with sulfone-based solvents, is crucial for optimizing electrolyte performance. Accurate force field parametrization is essential for simulating complex electrolyte systems with reliable predictive power. This study presents a robust workflow combining a genetic algorithm (GA) and Gaussian process regression (GPR) to develop optimized Lennard-Jones parameters for pure LiFSA, which are subsequently transferred to LiFSA-sulfone mixtures. The optimized parameters accurately capture nonbonded interactions and reproduce experimental transport properties, including viscosity and ionic conductivity, with deviations within 7.5%. Using the Green–Kubo formalism, viscosity and conductivity trends were computed and linked to solvation dynamics, revealing that mixtures containing symmetric sulfones (sulfolane and dimethyl sulfone) exhibit lower viscosities and higher conductivities compared to those with asymmetric sulfones (ethyl methyl sulfone and 3-methyl sulfolane). Analysis of relative coordination numbers further demonstrates the pivotal role of solvent oxygen (OS) in modulating ion transport, with enhanced OS coordination reducing viscosity and improving conductivity by facilitating ion mobility. This study provides a microscopic understanding of how ion–solvent interactions and solvation structures govern macroscopic transport behavior. The GA-GPR parametrization framework not only delivers transferable force fields capable of accurately predicting electrolyte properties but also offers practical insights for tailoring electrolytes with optimized performance in energy storage and conversion applications.