{
  "abstract": "Powder diffraction is a primary structural characterization tool in materials science, yet automated phase identification remains a major bottleneck for autonomous discovery. Existing workflows rely heavily on search–match heuristics and manual Rietveld refinement, and broadly usable end-to-end automation is especially limited for neutron powder diffraction, where comparable tools are largely absent. Here, we introduce Residual-Aware Deep-learning–Assisted Refinement for Powder Diffraction (RADAR-PD), a modality-aware machine learning framework for phase identification and quantification across both x-ray and neutron powder diffraction. RADAR-PD couples a mismatch-tolerant neural network operating on coarse momentum-transfer fingerprints with automated lattice nudging and physics-constrained Rietveld verification, enabling dominant-phase hypotheses to be generated from elemental constraints and secondary phases to be isolated recursively. On an experimental RRUFF powder x-ray diffraction benchmark, RADAR-PD outperforms data-driven automated Rietveld analysis (DARA) in recovering the reference phase. RADAR-PD further provides robust multiphase analysis on complex time-of-flight and constant-wavelength neutron datasets, addressing an important unmet need in automated neutron diffraction analysis. These results establish RADAR-PD as an auditable, instrument-agnostic framework for autonomous structural discovery.",
  "abstract_shown": true,
  "affiliations": [
    "Neutron Scattering Division, Oak Ridge National Laboratory , Oak Ridge, Tennessee 37831,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Lalit Yadav",
      "orcid": "0000-0002-0389-9424"
    },
    {
      "affs": [
        0
      ],
      "name": "Yongqiang Cheng",
      "orcid": "0000-0002-3263-4812"
    },
    {
      "affs": [
        0
      ],
      "name": "Mathieu Doucet",
      "orcid": "0000-0002-5560-6478"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/069xm9x5w027n.bib",
  "cited_by": 8,
  "doi": "10.1063/5.0345499",
  "fulltext_url": null,
  "id": "069xm9x5w027n",
  "issns": [
    "2770-9019"
  ],
  "landing_url": "https://doi.org/10.1063/5.0345499",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|X-ray Diffraction in Crystallography"
    ]
  ],
  "openalex": "W7160997105",
  "pdf_url": "https://pubs.aip.org/aip/aml/article-pdf/doi/10.1063/5.0345499/21123370/036114_1_5.0345499.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "American Institute of Physics",
  "ris": "https://research.draconitic.com/paper/069xm9x5w027n.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:21:06Z",
      "native_id": "W7160997105",
      "source": "openalex",
      "url": "https://doi.org/10.1063/5.0345499"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|X-ray Diffraction in Crystallography",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Automated multiphase identification and refinement in powder diffraction using mismatch-tolerant machine learning",
  "title_text": "Automated multiphase identification and refinement in powder diffraction using mismatch-tolerant machine learning",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:22Z",
  "url": "https://research.draconitic.com/paper/069xm9x5w027n",
  "venue": "APL Machine Learning",
  "versions": []
}