{
  "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 $167 \\pm 18$ meV, below the 200 meV barrier for vacancy-mediated Li migration in bulk Ga-LLZO. The corresponding intrinsic charge-transfer resistance is $\\sim 10^{-5}\\ Ω \\mathrm{cm}^{2}$, almost four orders of magnitude below the lowest experimentally reported $R_{\\mathrm{ct}}$. 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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Phys. Rev. Materials 10, 105404 (2026)",
  "arxiv": "2606.07772",
  "author_total": 3,
  "authors": [
    {
      "name": "Arseniy S. Burov"
    },
    {
      "name": "Artem M. Abakumov"
    },
    {
      "name": "Dmitry A. Aksyonov"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/51skvqy79ncec.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.07772",
  "fulltext_url": null,
  "id": "51skvqy79ncec",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.07772",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "2208|Advanced Battery Materials and Technologies"
    ]
  ],
  "openalex": "W7164033782",
  "pdf_url": "https://arxiv.org/pdf/2606.07772",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/51skvqy79ncec.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:11Z",
      "native_id": "2606.07772",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.07772"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Machine learning assisted molecular dynamics of charge-transfer mechanisms at Li/Ga-doped Li$_7$La$_3$Zr$_2$O$_{12}$ (LLZO) interfaces",
  "title_text": "Machine learning assisted molecular dynamics of charge-transfer mechanisms at Li/Ga-doped Li_7La_3Zr_2O_12 (LLZO) interfaces",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:11Z",
  "url": "https://research.draconitic.com/paper/51skvqy79ncec",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1103/mttl-2jq2",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}