{
  "abstract": "Accurately predicting the transport properties of liquid electrolytes is critical for next-generation battery design and requires force fields that are both quantum-mechanically accurate and fast enough for long-timescale simulation. Recent large-scale pretrained machine learning interatomic potentials (MLIPs) approach density-functional-theory accuracy, but remain too slow to access the necessary timescales to converge transport properties. We provide a knowledge distillation procedure to transfer the accuracy of a large-scale \"teacher\" MLIP into fast, system-specific \"students\", without any additional ab initio data. Our distilled models run ~40x faster, reproduce teacher-predicted structural observables, and produce stable 20 ns trajectories in days, rather than the months the teacher would require. Across a diverse set of sodium- and lithium-ion electrolytes, student-predicted self-diffusivities, ionic conductivities, and shear viscosities track experiment and outperform classical force-field predictions, establishing distillation as a practical route to accurate, long-timescale simulations necessary for high-throughput electrolyte screening.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.39513",
  "author_total": 13,
  "authors": [
    {
      "name": "Ishan Amin"
    },
    {
      "name": "Yue Jian"
    },
    {
      "name": "Nitesh Kumar"
    },
    {
      "name": "Christine Zhang"
    },
    {
      "name": "Jianwei Lai"
    },
    {
      "name": "Casey S. Mezerkor"
    },
    {
      "name": "Aaron M. Melemed"
    },
    {
      "name": "Jonathan R. Thurston"
    },
    {
      "name": "Neil P. Dasgupta"
    },
    {
      "name": "J. David Bazak"
    },
    {
      "name": "Ethan J. Crumlin"
    },
    {
      "name": "Samuel Blau"
    },
    {
      "name": "Aditi S. Krishnapriyan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fag4nndsbky89.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.39513",
  "fulltext_url": null,
  "id": "fag4nndsbky89",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.39513",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "arxiv",
      "cond-mat.soft"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.chem-ph"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.39513",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fag4nndsbky89.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:23Z",
      "native_id": "2610.39513",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.39513"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Distilling Large-Scale Machine Learning Interatomic Potentials to Predict Long-Timescale Electrolyte Transport Properties",
  "title_text": "Distilling Large-Scale Machine Learning Interatomic Potentials to Predict Long-Timescale Electrolyte Transport Properties",
  "type": "preprint",
  "updated_at": "2026-11-01T12:20:45Z",
  "url": "https://research.draconitic.com/paper/fag4nndsbky89",
  "venue": "arXiv",
  "versions": []
}