Machine learning assisted molecular dynamics of charge-transfer mechanisms at Li/Ga-doped Li7La3Zr2O12 (LLZO) interfaces
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 are performed at 0 K along a single minimum-energy path and therefore neglect concerted multi-ion motion 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 across Li/SE interfaces. We also introduce a residence-time window method that filters out ion rattling at the interface and isolates genuine charge-transfer events. The resulting charge-transfer activation energy at the Li/Ga-LLZO interface is only 167 ± 18 meV, below the 200 meV barrier for vacancy-mediated Li migration in bulk Ga-LLZO. The corresponding intrinsic charge-transfer resistance is ∼ 10−5 Ω cm2, almost four orders of magnitude below the lowest experimentally reported Rct. These results indicate that intrinsic charge transfer across the Li/Ga-LLZO interface is not rate-limiting, and that Li transport within the electrolyte instead governs the overall kinetics. Taken together, our findings clarify the fast interfacial kinetics in Li/LLZO systems, and the proposed methodology can aid further interface optimization in solid-state batteries.
Versions
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BibTeX
@misc{burov2026machine,
title = {{Machine learning assisted molecular dynamics of charge-transfer mechanisms at Li/Ga-doped Li$_7$La$_3$Zr$_2$O$_12$ (LLZO) interfaces}},
author = {Arseniy S. Burov and Artem M. Abakumov and Dmitry A. Aksyonov},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2606.07772},
eprint = {2606.07772},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.07772},
}
RIS
TY - UNPB
TI - Machine learning assisted molecular dynamics of charge-transfer mechanisms at Li/Ga-doped Li_7La_3Zr_2O_12 (LLZO) interfaces
AU - Arseniy S. Burov
AU - Artem M. Abakumov
AU - Dmitry A. Aksyonov
PY - 2026
DA - 2026/09/20
PB - arXiv
DO - 10.48550/arxiv.2606.07772
UR - https://arxiv.org/abs/2606.07772
AB - 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 are performed at 0 K along a single minimum-energy path and therefore neglect concerted multi-ion motion 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 across Li/SE interfaces. We also introduce a residence-time window method that filters out ion rattling at the interface and isolates genuine charge-transfer events. The resulting charge-transfer activation energy at the Li/Ga-LLZO interface is only 167 ± 18 meV, below the 200 meV barrier for vacancy-mediated Li migration in bulk Ga-LLZO. The corresponding intrinsic charge-transfer resistance is ∼ 10^−5 Ω cm^2, almost four orders of magnitude below the lowest experimentally reported R_ct. These results indicate that intrinsic charge transfer across the Li/Ga-LLZO interface is not rate-limiting, and that Li transport within the electrolyte instead governs the overall kinetics. Taken together, our findings clarify the fast interfacial kinetics in Li/LLZO systems, and the proposed methodology can aid further interface optimization in solid-state batteries.
ER -