{
  "abstract": "Amorphous materials are statistical ensembles rather than definitive structures, and conventional density-functional (DFT) and machine-learned-potential simulations sample only a small part of that ensemble. We present an $SE(3)$-equivariant denoising-diffusion model that learns the configurational distribution of amorphous oxides, so the model itself is the structure database. The learning is data efficient. A model trained on $1{,}781$ DFT configurations suffices to reproduce partial radial distribution functions, coordination statistics and bond-angle distributions, and to generate models of over $3\\times10^{5}$ atoms at a cost comparable to that of the cheapest classical pair potentials. The trained model can propose amorphous atomic structures for first-principles relaxation to explore the configuration space. For example, it locates an amorphous Zr-Ta-O structure $36$ meV/atom below the previously known minimum. Generation can also extend beyond trained conditions to non-stoichiometric compositions, other mass densities, interfaces, and doping. First-principles verification confirms that generation can be steered to a requested energy, and shows that the denoising training loss does not rank generative quality, because the two measure different things.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.03973",
  "author_total": 4,
  "authors": [
    {
      "name": "Jun Jiang"
    },
    {
      "name": "Ian Berry"
    },
    {
      "name": "James N. Fry"
    },
    {
      "name": "Hai-Ping Cheng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5ymhgfqm5sa64.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.03973",
  "fulltext_url": null,
  "id": "5ymhgfqm5sa64",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.03973",
  "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",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7220673285",
  "pdf_url": "https://arxiv.org/pdf/2610.03973",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5ymhgfqm5sa64.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:01Z",
      "native_id": "2610.03973",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.03973"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Equivariant generative diffusion learns and generalizes the structural ensemble of amorphous oxides",
  "title_text": "Equivariant generative diffusion learns and generalizes the structural ensemble of amorphous oxides",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:01Z",
  "url": "https://research.draconitic.com/paper/5ymhgfqm5sa64",
  "venue": "arXiv",
  "versions": []
}