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arXiv · 2606.07772

Machine learning assisted molecular dynamics of charge-transfer mechanisms at Li/Ga-doped Li$_7$La$_3$Zr$_2$O$_{12}$ (LLZO) interfaces

Arseniy S. Burov·Artem M. Abakumov·Dmitry A. Aksyonov
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Abstract

Interfacial charge transfer between solid electrolytes (SEs) and Li metal is a key factor limiting all-solid-state battery performance. Conventional density functional theory and nudged elastic band calculations neglect many-body correlations and finite-temperature effects, which can lead to inaccurate activation barriers. Here, we trained moment tensor potentials (MTPs) for garnet LLZO systems (t-LLZO, c-LLZO, and Ga-LLZO) and Li metal, enabling machine-learning molecular dynamics (MLMD) simulations of Li$^+$ diffusion in the bulk and at Li/SE interfaces. We also introduce a residence-time window method that filters out ion rattling and isolates genuine charge-transfer events. The resulting charge-transfer activation energies are low: 167 meV at the Li/Ga-LLZO interface and 200 meV in Ga-LLZO, corresponding to resistances of $\sim \, 10^{-5} \, \Omega \,\mathrm{cm}^{2}$. These results indicate that intrinsic Li/Ga-LLZO charge transfer is not rate-limiting. Overall, our findings clarify the fast interfacial kinetics in Li/LLZO systems, and the proposed methodology can aid further interface optimization in solid-state batteries.

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Arseniy S. Burov, Artem M. Abakumov, Dmitry A. Aksyonov. 2026-06-05. Machine learning assisted molecular dynamics of charge-transfer mechanisms at Li/Ga-doped Li$_7$La$_3$Zr$_2$O$_{12}$ (LLZO) interfaces. https://arxiv.org/abs/2606.07772

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