Unraveling Lithium Dynamics in Solid Electrolyte Interphase: From Graph Contrastive Learning to Transport Pathways
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866908861189849088 |
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| author | Guan, Qiye Cai, Yongqing |
| author_facet | Guan, Qiye Cai, Yongqing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02284 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Unraveling Lithium Dynamics in Solid Electrolyte Interphase: From Graph Contrastive Learning to Transport Pathways Guan, Qiye Cai, Yongqing Materials Science Statistical Mechanics Chemical Physics Computational Physics 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. |
| title | Unraveling Lithium Dynamics in Solid Electrolyte Interphase: From Graph Contrastive Learning to Transport Pathways |
| topic | Materials Science Statistical Mechanics Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2603.02284 |