{
  "abstract": "We develop a machine-learned neuroevolution potential (NEP) for the Mo-S-Al-O system, targeting MoS$_2$ heteroepitaxy on c-plane sapphire, $α$-Al$_2$O$_3$(0001). The potential is trained on 63,611 PBE+D3 structures distilled from about 1.1 million curated density-functional-theory (DFT) configurations, using an energy-difference loss function that directly targets interfacial and orientation-dependent energies. On held-out data, the energy and force errors are 29 meV/atom and 0.23 eV/$\\mathring{A}$. Validated against DFT on a suite of physical properties, the NEP reproduces the lattice constants within 0.3 %, the elastic constants within 17 %, the phonon dispersions with mean deviations below 2.5%, and the surface energies within 1.3 %. At the MoS$_2$/sapphire interface, it reproduces the adhesion energy minimum within 0.8 %, the interfacial energy within 2.2 % and the most stable orientation of the monolayer. It preserves the DFT-predicted energetic ordering of edges, point defects (Spearman $ρ$ = 0.995) and mirror-twin boundaries ($ρ$ = 1.0), and reproduces tilt grain-boundary energies within 8 % ($ρ$ = 0.98) and stacking-fault energy barriers within 14 %. The resulting potential enables future studies of the atomic-scale mechanisms of defect formation in two-dimensional heteroepitaxy.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.02403",
  "author_total": 4,
  "authors": [
    {
      "name": "Emir Bilgili"
    },
    {
      "name": "Nicholas Taormina"
    },
    {
      "name": "Simon R. Phillpot"
    },
    {
      "name": "Youping Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/874dm89rmxdfz.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.02403",
  "fulltext_url": null,
  "id": "874dm89rmxdfz",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.02403",
  "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.comp-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.02403",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/874dm89rmxdfz.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:59Z",
      "native_id": "2611.02403",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.02403"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Machine-Learned Interatomic Potential for the Mo-S-Al-O System",
  "title_text": "Machine-Learned Interatomic Potential for the Mo-S-Al-O System",
  "type": "preprint",
  "updated_at": "2026-11-03T12:20:59Z",
  "url": "https://research.draconitic.com/paper/874dm89rmxdfz",
  "venue": "arXiv",
  "versions": []
}