{
  "abstract": "Magnetic frustration is commonly quantified by the frustration index, calculated from the Curie-Weiss and Neel temperatures. Because both quantities are not simultaneously available for most materials, direct prediction of the magnetic frustration index remains challenging. Here, we propose a machine learning workflow for indirect prediction of the frustration index and consequent screening of potential frustrated magnetic compounds through independent prediction of Curie-Weiss and Neel temperatures. In our work, composition-based models implemented via the CatBoost algorithm, trained on experimental data were optimized using chemically informed dataset subdivision. The highest predictive accuracy was achieved for the rare earth-oxide model in predicting Neel and for the rare earth model in predicting Curie-Weiss. Ensemble uncertainty was incorporated through a conservative frustration index to improve the reliability of candidate selection. Screening of approximately 67000 Materials Project compounds identified 20 high-confidence frustrated magnetic candidates, including several experimentally known frustrated magnets and previously unexplored oxide systems potential for further investigation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.15683",
  "author_total": 5,
  "authors": [
    {
      "name": "Regina Burganova"
    },
    {
      "name": "Irina Piyanzina"
    },
    {
      "name": "Ruslan Akhmerov"
    },
    {
      "name": "Alexander Sigov"
    },
    {
      "name": "Maxim Fedorov"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a0tvx7zp8am8w.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.15683",
  "fulltext_url": null,
  "id": "a0tvx7zp8am8w",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.15683",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.15683",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a0tvx7zp8am8w.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:06Z",
      "native_id": "2610.15683",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.15683"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Indirect prediction and screening of frustrated magnetic materials using machine learning",
  "title_text": "Indirect prediction and screening of frustrated magnetic materials using machine learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:06Z",
  "url": "https://research.draconitic.com/paper/a0tvx7zp8am8w",
  "venue": "arXiv",
  "versions": []
}