Abstract
High interfacial impedance hinders the development of all-solid-state batteries. While thermodynamic
analyses offer stability insights, they overlook the kinetic effects that dominate during operation. Here,
we use a machine learning interatomic potential to perform long-timescale molecular dynamics
simulations of various cathode/solid electrolyte (SE) interfaces, including sulfide, chloride, and oxide SEs
with layered LiCoO2. Our simulations reveal three primary kinetic mechanisms driving impedance: (1)
interfacial reactions, especially with sulfide SEs, forming poorly conducting interphases; (2) the formation
of lithium-depleted regions that reduce available Li+ pathways; and (3) cation inter-diffusion, which
obstructs lithium transport channels and degrades the cathode. These findings underscore the critical
role of kinetics in interfacial stability and establish machine learning-driven atomistic modeling as
a powerful tool for designing next-generation solid-state batteries.
© The Royal Society of Chemistry 2025
© The Royal Society of Chemistry 2025
| Original language | English |
|---|---|
| Pages (from-to) | 38919–38927 |
| Number of pages | 9 |
| Journal | Journal of Materials Chemistry A |
| Volume | 13 |
| Issue number | 45 |
| Online published | 23 Oct 2025 |
| DOIs | |
| Publication status | Published - 7 Dec 2025 |
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