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

TL;DR

This study introduces a machine learning molecular dynamics framework to accurately simulate early SEI formation in lithium metal batteries, revealing how electrolyte concentration influences interphase composition and stability.

Contribution

The paper presents a novel Deep Potential-based MLMD method trained on ab initio data, enabling quantum-accurate modeling of SEI nucleation and growth at lithium interfaces.

Findings

3.5 M LiTFSI induces thick, O/F-rich SEIs with rapid growth.

Lower concentrations form thinner, LiF-rich interphases with slower kinetics.

Model results align with experimental data on cycling stability and passivation.

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.