{
  "abstract": "The structural and spectroscopic record of a high-throughput synthesis campaign exceeds what experimenters can closely and accurately analyze with conventional tools alone. We report a workflow built for this problem: a language model agent operates an automated Rietveld phasesearch pipeline, Crystalyze Match, built on the open-source DARA engine, and then interprets the phases, optical spectra, and synthesis metadata. We demonstrate this workflow on a 48 sample campaign for the persistent phosphor Ca 2 ZnSi 2 O 7 :Eu 2+ ,Nd 3+ (doped hardystonite), a water-stable candidate alternative to SrAl 2 O 4 :Eu 2+ ,Dy 3+ for luminescent road markings. Within the campaign’s lifetime, the workflow mapped two decomposition pathways, incomplete reaction and zinc volatilization, that validate the two-step synthetic approach. The workflow also identified the fate of the rare-earth dopants: a silicate oxyapatite, Ca 2 (Eu,Nd) 8 (SiO 4 ) 6 O 2 , missed by prior manual analysis, is present in every doped sample. Human adjudication proved necessary as the search first scored the oxyapatite through a lanthanide-free proxy structure that scatters too weakly to register at low fractions, and a corrected-model re-screen of all 48 patterns recovered it. Though the rare-earth dopants appear to end up in the oxyapatite phase, emission rises monotonically with Eu content, and the material shows the target persistent luminescence, so where the emitting Eu 2+ resides remains unresolved. Our work highlights that human review and re-screening after every structure-model revision dramatically improve the trustworthiness of campaign-scale claims.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Materials Science and Engineering",
    "University of Utah",
    "83 Sciences"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Ziyang Jiang"
    },
    {
      "affs": [
        2
      ],
      "name": "Eric Riesel",
      "orcid": "0000-0001-5715-7234"
    },
    {
      "affs": [
        2
      ],
      "name": "Ian Naccarella"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Taylor D. Sparks"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8svayeav0qm6y.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009032/v1",
  "fulltext_url": null,
  "id": "8svayeav0qm6y",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009032/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": "W7213554366",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009032/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/8svayeav0qm6y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15009032",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009032/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Agentic Workflow for High-Throughput Synthesis Campaigns: Mapping the Persistent Phosphor Ca2ZnSi2O7:Eu2+,Nd3+",
  "title_text": "Agentic Workflow for High-Throughput Synthesis Campaigns: Mapping the Persistent Phosphor Ca2ZnSi2O7:Eu2+,Nd3+",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/8svayeav0qm6y",
  "venue": "ChemRxiv",
  "versions": []
}