{
  "abstract": "Designing new materials is often slow and experimentally intensive, with structural characterization representing a particular bottleneck. Recent advances in machine learning (ML) aim to accelerate this process by predicting plausible atomic structures describing a material directly from experimental data. Here we present the total scattering conditioned variational autoencoder (ToSCA), a generative ML model for predicting plausible atomic structures of metal oxide nanomaterials from total scattering and pair distribution function (PDF) data. ToSCA learns a chemically meaningful latent representation and predicts unit cells within tolerances suitable for subsequent real-space refinement. The model also shows behavior consistent with learning structural relationships between related crystal types, and can correctly place the new CHILI-Interpolation dataset in the latent space. Finally, we demonstrate that ToSCA is able to retain useful performance in a realistic use-case by testing it on 11 experimental PDFs from 3 crystal types, including test cases outside of the training distribution.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemistry & Nano-Science Center",
    "University of Copenhagen",
    "Department of Computer Science"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Ulrik Friis-Jensen",
      "orcid": "0000-0001-6154-1167"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Frederik L. Johansen",
      "orcid": "0000-0002-8049-8624"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "Erik B. Dam",
      "orcid": "0000-0002-8888-2524"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "Raghavendra Selvan",
      "orcid": "0000-0003-4302-0207"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Kirsten M. Ø. Jensen",
      "orcid": "0000-0003-0291-217X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/efa3370nt6kx6.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009767/v1",
  "fulltext_url": null,
  "id": "efa3370nt6kx6",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009767/v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7216038512",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/efa3370nt6kx6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009767",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009767/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "ToSCA: Total Scattering Conditioned Variational Autoencoder for Atomic Structure Prediction from Pair Distribution Function Data",
  "title_text": "ToSCA: Total Scattering Conditioned Variational Autoencoder for Atomic Structure Prediction from Pair Distribution Function Data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/efa3370nt6kx6",
  "venue": "ChemRxiv",
  "versions": []
}