{
  "abstract": "Inverting a one-dimensional powder X-ray diffraction (PXRD) pattern to a three-dimensional crystal structure remains an open problem, and recent generative approaches report results under mutually incompatible protocols with failure modes seldom catalogued. PXRD-Diff, a 3.7 M-parameter conditional diffusion model trained on the CDVAE MP-20 split, is presented, and DiffractGPT, PXRDnet and deCIFer are reproduced through the same pymatgen StructureMatcher harness. The central, statistically robust finding is mechanistic: the learned PXRD encoder reaches a 0.007 normalised auxiliary lattice-regression loss in isolation, yet substituting that head’s own lattice into the sampler recovers only 1.2 % of structures, because its ~1.1 Å length error shifts every Bragg peak by ~15 %. The limiter is the global pooling that discards absolute peak positions, not the denoiser alone. A perturbation study locates a sharp sensitivity knee at ~0.5 Å cell error, and a true-lattice oracle lifts match to 4.5 % at n = 1000 (three seeds; 95 % Wilson confidence interval [3.8, 5.3]), disjoint from the no-true-lattice indexer’s 1.5 % [1.2, 2.0]. Replacing the learned lattice head with a classical Q-space autoindexer raises no-true-lattice match to 1.5 % from a learned head that recovers essentially nothing; a three-seed paired McNemar test confirms the gain is significant (p < 10−4; 27 fixed, none broken), concentrated in the high-symmetry systems below the sensitivity knee. Reproduced on one harness, deCIFer reaches 73.8 % match, DiffractGPT 18.9 % and PXRDnet 30.0 %, leaving PXRD-Diff 12–46 times behind. The contributions are the encoder-bottleneck diagnosis, the classical-indexer drop-in, a four-item failure catalogue and the shared evaluation harness; code, checkpoints and the differentiable Bragg simulator are released.",
  "abstract_shown": true,
  "affiliations": [
    "Purdue University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Frank Cai",
      "orcid": "0009-0003-0041-1459"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2m46mzmswb0x4.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008282/v1",
  "fulltext_url": null,
  "id": "2m46mzmswb0x4",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008282/v1",
  "language": null,
  "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|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7208730883",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008282/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/2m46mzmswb0x4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008282",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008282/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "PXRD-Diff: diagnosing the encoder bottleneck in diffusion-based powder-diffraction structure inversion, with classical autoindexing as a drop-in remedy",
  "title_text": "PXRD-Diff: diagnosing the encoder bottleneck in diffusion-based powder-diffraction structure inversion, with classical autoindexing as a drop-in remedy",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/2m46mzmswb0x4",
  "venue": "ChemRxiv",
  "versions": []
}