{
  "abstract": "We develop a machine learning assisted workflow for the rapid screening of permanent magnet candidates within the CaCu$_5$ structure family. We generated more than 60,000 structures through substitutions. These compositions were screened using a combination of pretrained materials models, Materials Project data, and a machine-learning model for magnetization prediction. Four criteria, namely, metallicity, negative formation energy, energy above the convex hull $\\leq 50$~meV/atom, and predicted magnetization above the adopted $\\geq 0.75$~T threshold reduced the initial chemical space to 439 candidates for first-principles calculations. Density functional theory calculations revealed that 159 compounds exhibit positive axial magnetic anisotropy energy, favoring [001] over the tested in-plane directions. The resulting dataset reveals chemical trends in magnetic anisotropy across the CaCu$_5$ compositional space and provides a set of candidates for further investigation. More broadly, this work demonstrates a practical strategy for combining existing machine learning models with first-principles calculations to systematically reduce large chemical spaces to tractable sets for computationally intensive materials screening.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.10891",
  "author_total": 3,
  "authors": [
    {
      "name": "Nabaraj Pokhrel"
    },
    {
      "name": "Sheila Whitman"
    },
    {
      "name": "David S. Parker"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8p84y4x2j1ch8.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.10891",
  "fulltext_url": null,
  "id": "8p84y4x2j1ch8",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.10891",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7221150548",
  "pdf_url": "https://arxiv.org/pdf/2610.10891",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8p84y4x2j1ch8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:54Z",
      "native_id": "2610.10891",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.10891"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Data driven screening of CaCu$_5$-type magnetic structures",
  "title_text": "Data driven screening of CaCu_5-type magnetic structures",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:54Z",
  "url": "https://research.draconitic.com/paper/8p84y4x2j1ch8",
  "venue": "arXiv",
  "versions": []
}