Predictive Machine Learning Molecular Dynamics of SEI Formation in Concentrated LiTFSI and LiPF6 Electrolytes for Lithium Metal Batteries

Abstract

High-energy-density lithium metal batteries require electrolytes that enable fast ion transport and form a stable solid-electrolyte interphase (SEI) to sustain high-rate cycling, a process that remains challenging to capture experimentally. Here, we develop a Deep Potential-based machine learning molecular dynamics (MLMD) framework, trained on extensive ab initio datasets and validated against experimental transport properties, to resolve early-stage SEI nucleation at lithium metal interfaces with quantum accuracy. We find that at the Li-metal interface, 3.5 M LiTFSI/DMC induces spontaneous, thermally activated reduction reactions, yielding rapidly growing thick anion-derived SEIs enriched in O/F-containing species. In contrast, 1.5-2.5 M LiTFSI/DMC and 1 M LiPF6/EMC/DMC/EC form thinner, LiF-dominated interphases with slower growth kinetics. Our modeling results are consistent with experimental observations, where 3.5 M LiTFSI enhances cycling stability and rate capability, while lower concentrations result in weaker passivation. Our MLMD framework efficiently captures the electrolyte transport and early-stage SEI formation mechanisms in LMBs.

Figures (4)

• Figure 1: Multiscale Simulation Framework and Validation. (A) Integrated workflow spanning ab initio data generation, Deep Potential (DP) model construction, multiscale validation against experimental and classical baselines, and large-scale interfacial simulations. (B) Parity plots comparing DP-predicted energies and forces with AIMD reference data. (C) Radial distribution functions demonstrating structural consistency between MLMD and AIMD trajectories.
• Figure 2: Validation of Liquid Structure and Transport Properties. (A) Comparison of radial distribution functions and (B) running coordination numbers predicted by CMD versus MLMD. (C) Ionic conductivity, (D) ion self-diffusion coefficients, and (E) viscosity. Conductivity and viscosity values are benchmarked against experimental data from Ref. li2023concentrated.
• Figure 3: Early-Stage SEI Nucleation and Growth. (A) Representative simulation snapshots of the interfacial structure for 3.5 M LiTFSI and 1 M LiPF6 electrolytes. (B) Temporal evolution of interfacial Li--X bond counts. (C, D) Interfacial radial distribution functions, $g(r)$, and running coordination numbers, $N(r)$, for (C) 3.5 M LiTFSI and (D) 1 M LiPF6 systems.
• Figure 4: Temporal Evolution of Interfacial Composition. Oxygen and fluorine atomic density profiles along the surface normal ($z$-direction) for (A) 1.5 M LiTFSI, (B) 2.5 M LiTFSI, (C) 3.5 M LiTFSI, and (D) 1 M LiPF6 electrolytes. (E) Schematic illustration summarizing the divergent mechanisms of SEI nucleation and growth observed across different salt chemistries and concentrations.