{
  "abstract": "Machine-learned interatomic potentials trained across the periodic table have made atomistic simulation broadly accessible, and specializing them to a single compound class is widely expected to improve accuracy. Yet examples remain scarce, and fewer still surpass universal models in speed or reach a higher level of electronic-structure theory. Here we present a potential that is universal within the class of water-free solid-state hydrogen-bond network mediated proton conductors rather than across chemistry and a database of 4.4 million first-principles configurations spanning 55 materials. It surpasses leading general-purpose potentials across this domain, recovering measured activation energies and the ordering of anion rotational dynamics that those models miss; agreement on static structure does not imply agreement on transport. Accuracy falls for compositions far from the training set but stays competitive for close structural relatives, and a higher level of electronic-structure theory is reached with a few hundred additional configurations per material. We further introduce a compact variant carrying a fifth of the parameters, faster still and yet more accurate than every general-purpose model tested. It sustains more than a quarter of a million atoms on a single graphics processor, placing grain boundaries and the transition into the highly conducting phase within reach, and making a quantum treatment of the protons affordable. Reference accuracy and accessible system size thus become largely independent, offering a template for other compound classes.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.38531",
  "author_total": 3,
  "authors": [
    {
      "name": "Jonas Hänseroth"
    },
    {
      "name": "Rose Asuka Baroness von Stackelberg"
    },
    {
      "name": "Christian Dreßler"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1f6mwbesvkk1h.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.38531",
  "fulltext_url": null,
  "id": "1f6mwbesvkk1h",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.38531",
  "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"
    ],
    [
      "arxiv",
      "physics.chem-ph"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7215077794",
  "pdf_url": "https://arxiv.org/pdf/2609.38531",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1f6mwbesvkk1h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:48Z",
      "native_id": "2609.38531",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.38531"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Enabling Domain-Specific Atomistic Models: A Machine Learning Potential for the Solid Acid Family",
  "title_text": "Enabling Domain-Specific Atomistic Models: A Machine Learning Potential for the Solid Acid Family",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:48Z",
  "url": "https://research.draconitic.com/paper/1f6mwbesvkk1h",
  "venue": "arXiv",
  "versions": []
}