{
  "abstract": "Dopant site occupancy bridges chemical composition, local crystal structure, and macroscopic functional responses in M-type hexaferrites. However, reliable occupancy hypotheses are difficult to establish before structural refinement. Here, we develop a pre-refinement machine-learning framework using 494 dopant-level records extracted from refined structures. This workflow combines Ba/Sr-vs.-Fe sublattice classification, fractional occupancy prediction over five inequivalent Fe sites, and reliability-aware assessment. The CatBoost classifier achieves 96% accuracy on external validation for sublattice assignment. For Fe-sublattice dopants, homogeneous occupancy-model screening and heterogeneous cooperative occupancy-model screening yield a macro mean absolute error as low as 0.0584, while preserving dominant-site identification and occupancy ranking. The results show that dopant site occupancy is jointly controlled by dopant chemistry, target-dopant amount, and co-doping environment rather than a universal site-preference rule. By integrating TabPFN baseline comparison, training-experience assessment, and model-consistency analysis, the framework identifies boundary and extrapolative samples that require priority refinement. Our predictions for W-doped and Sc-doped M-type hexaferrites successfully reproduce key experimental occupancy trends. This work offers a practical data-driven strategy to generate physically plausible occupancy hypotheses and assign refinement priorities for partially occupied oxide systems.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Physics, Beijing Technology and Business University, Beijing, China.",
    "Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.",
    "Feicui Lake Laboratory, School of Materials Science and Engineering, Anhui University, Hefei, China.",
    "Spallation Neutron Source Science Center (SNSSC), Dongguan, China.",
    "State Key Laboratory of Magnetism, Institute of Physics, Chinese Academy of Sciences, Beijing, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Dan Liu",
      "orcid": "0000-0003-4002-0690"
    },
    {
      "affs": [
        0
      ],
      "name": "Jianyu Ren"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Ruoshui Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yinong Yin"
    },
    {
      "affs": [
        0
      ],
      "name": "Jianfeng Xi"
    },
    {
      "affs": [
        2
      ],
      "name": "Xin Ming"
    },
    {
      "affs": [
        3
      ],
      "name": "Jiazheng Hao"
    },
    {
      "affs": [
        4
      ],
      "name": "Tongyun Zhao"
    },
    {
      "affs": [
        4
      ],
      "name": "Fengxia Hu"
    },
    {
      "affs": [
        2,
        4
      ],
      "name": "Baogen Shen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/03vs3tzqh874c.bib",
  "cited_by": 0,
  "doi": "10.1002/advs.78493",
  "fulltext_url": "https://europepmc.org/articles/PMC13720265",
  "id": "03vs3tzqh874c",
  "issns": [
    "2198-3844"
  ],
  "landing_url": "https://europepmc.org/article/MED/42958698",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research Article"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7224778734",
  "pdf_url": "https://europepmc.org/articles/PMC13720265?pdf=render",
  "pmcid": "PMC13720265",
  "pmid": "42958698",
  "published": "2026-10-29",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/03vs3tzqh874c.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T00:20:36Z",
      "native_id": "MED:42958698",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42958698"
    },
    {
      "first_seen": "2026-11-03T12:21:11Z",
      "native_id": "PMC13720265",
      "source": "pmc",
      "url": "https://doi.org/10.1002/advs.78493"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Pre-Refinement Prediction of Dopant Site Occupancy in M-Type Hexaferrites via Hierarchical and Uncertainty-Aware Machine-Learning.",
  "title_text": "Pre-Refinement Prediction of Dopant Site Occupancy in M-Type Hexaferrites via Hierarchical and Uncertainty-Aware Machine-Learning.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:21:11Z",
  "url": "https://research.draconitic.com/paper/03vs3tzqh874c",
  "venue": "Advanced science (Weinheim, Baden-Wurttemberg, Germany)",
  "versions": []
}