{
  "abstract": "Neutron diffraction is a versatile experimental technique capable of probing a material's magnetic properties. While diffraction is typically used to determine the magnetic structure of a material, magnetic diffuse scattering data from a diffraction experiment are also sensitive to the magnetic interactions in its Hamiltonian. However, accurately determining magnetic interaction parameters from neutron-scattering data involves an inverse scattering problem that is challenging to solve in general. Here, we investigate the effectiveness of a machine learning approach to predict the interaction parameters given magnetic diffuse-scattering data measured on powder samples, for a comprehensive survey of isotropic interactions on eight high-symmetry lattices. Across all lattices we considered, the machine-learning approach estimates the interaction parameters with high (~2%) accuracy, while avoiding the issue of false minima that is encountered with non-linear least squares refinement. Our results highlight that powder diffuse-scattering data can provide a compact \"fingerprint\" of the magnetic interactions for many materials.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.21970",
  "author_total": 3,
  "authors": [
    {
      "name": "Adit S. Desai"
    },
    {
      "name": "Yongqiang Cheng"
    },
    {
      "name": "Joseph A. M. Paddison"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1xqc6327ffp17.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.21970",
  "fulltext_url": null,
  "id": "1xqc6327ffp17",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.21970",
  "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",
      "cond-mat.str-el"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7213878673",
  "pdf_url": "https://arxiv.org/pdf/2609.21970",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1xqc6327ffp17.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:03Z",
      "native_id": "2609.21970",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.21970"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Machine learning magnetic interactions from neutron powder diffraction data",
  "title_text": "Machine learning magnetic interactions from neutron powder diffraction data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:03Z",
  "url": "https://research.draconitic.com/paper/1xqc6327ffp17",
  "venue": "arXiv",
  "versions": []
}