Structural and Transport Properties of LiTFSI/G3 Electrolyte with Machine-Learned Molecular Dynamics

CY Cao and LY Bai and S Cao and Y Su and YZ Wang and ZY Fan and P Qian, JOURNAL OF PHYSICAL CHEMISTRY C, 129, 13030-13039 (2025).

DOI: 10.1021/acs.jpcc.5c02287

The lithium bis(trifluoromethylsulfonyl)azanide-triglyme (LiTFSI/G3) electrolyte plays a critical role in the performance of lithium-ion batteries. However, its solvation structure and transport properties at the atomic scale remain incompletely understood. In this study, we develop an efficient and accurate neuroevolution potential (NEP) model by integrating bootstrap and active learning strategies. Using machine- learned NEP-driven molecular dynamics simulations, we explore the structural and diffusion properties of LiTFSI/G3 across a wide range of the solute-to-solvent ratios, systematically analyzing electrolyte density, ion coordination, viscosity, and lithium self-diffusion. The computed densities show excellent agreement with experimental data, and pair correlation analysis reveals significant interactions between lithium ions and surrounding oxygen atoms, which strongly impact Li+ mobility. Viscosity and diffusion calculations further demonstrate that increasing LiTFSI concentration enhances Li-O interactions, resulting in higher viscosity and reduced lithium diffusion. Additionally, machine learning-based path integral molecular dynamics (PIMD) simulations confirm the small impact of quantum effects on Li+ transport. The electrolyte-specific protocol developed in this work provides a systematic framework for constructing high-fidelity machine learning potentials for complex systems.

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