Unraveling Lithium Dynamics in Solid Electrolyte Interphase: From Graph Contrastive Learning to Transport Pathways

TL;DR

This paper introduces GET-SEI, a comprehensive framework combining graph contrastive learning, dynamic mode decomposition, and transition path theory to analyze lithium transport mechanisms in solid electrolyte interphases, aiding battery optimization.

Contribution

The study develops GET-SEI, a novel, general, and interpretable framework for characterizing local environments and transport pathways in solid electrolyte interphases without predefined labels.

Findings

Identifies dominant lithium transport pathways in various SSE systems.

Quantifies kinetic bottlenecks affecting lithium mobility.

Provides metrics for evaluating lithium transport efficiency.

Abstract

Fast lithium transport across the solid-state electrolyte (SSE)/lithium metal anode interface is critical for high-performance all-solid-state batteries. Uncovering the complex lithium dynamics governed by diverse local environments in the solid electrolyte interphase (SEI) is fundamental for performance optimization. However, a general framework for characterizing these distinct local environments and the associated transport mechanisms remains lacking. Here, we develop GET-SEI, a general framework that discovers local atomic environments without predefined labels through Graph contrastive learning (GCL), models lithium transition kinetics via Extended dynamic mode decomposition (EDMD), and quantifies reactive lithium flux through Transition path theory (TPT). Applied to different SSE/Li systems, including sulfides (Li6PS5Cl/Li, Li10GeP2S12/Li) and oxides (Li7La3Zr2O12/Li), the GET-SEI reveals dominant transport pathways and kinetic bottlenecks in each system, providing quantitative metrics for evaluating lithium transport efficiency. As novel high-performance SSEs continue to emerge, GET-SEI offers a widely applicable, interpretable tool for targeted SEI engineering.