{
  "abstract": "Multi-scale simulations of semiconductor alloys rest on foundation interatomic potentials (machine-learned force models trained on large density-functional databases), re-released as training data grow. A release changes defect energies and phonon stiffness and, through them, the recipes and design verdicts downstream; recomputing everything is costly, and recomputing nothing leaves conclusions that may no longer hold. Workflow records list every result downstream of a calculation, not which could change a decision. We tested whether the conclusions a release can change are predictable from a campaign’s provenance record (a record of which calculation fed which result) before any rerun and settled by recomputing only the predicted units. The chain served a SiGeC design question (germanium content, carbon content and SiC particle size for low lattice thermal conductivity), with MACE-MP-0 replaced by MACE-MPA-0 and all predictions and decision rules fixed in advance. From the record, 49 of 128 record entries were reached, 24 conclusions were classified by their margin to a decision threshold, and four recompute units were named. Recomputing those units (70 relaxations, 91 molecular-dynamics runs) settled 20 conclusions: none changed outside the predicted set (exact 95% upper bound on the miss rate 0.24), eight of nine flagged as at risk held, and one, Ge attraction to the first shell around a SiC particle, reversed by less than a millielectronvolt past its threshold, while the conclusion it served (no germanium-depleted zone forms) held. Magnitudes moved more than assumed: the energy carbon gains by precipitating as SiC grew by 0.65–0.75 eV against an assumed 0.3 eV; the recipe and the germanium saving held. Recomputation cost 3.5 CPU-task-hours and 354 GPU-hours, 2.2 times the GPU estimate scaled from the recorded runs. Decisions whose margins exceed a measured release shift can be carried across a release with a recorded check; magnitudes need recomputation, and explicit records of each result’s inputs and code would make the prediction a lookup.",
  "abstract_shown": true,
  "affiliations": [
    "Materials Science and Engineering, University of Utah, 122 S. Central Campus Drive, Rm 304, Salt Lake City, UT, United States of America, 84112-0063"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Kinston Ackölf",
      "orcid": "0000-0002-5579-4878"
    },
    {
      "affs": [
        0
      ],
      "name": "Taylor D. Sparks"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/afctrbnh65wxh.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010670/v1",
  "fulltext_url": null,
  "id": "afctrbnh65wxh",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010670/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": "W7222482602",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010670/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/afctrbnh65wxh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15010670",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010670/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Provenance records predict which multi-scale conclusions a foundation-potential release can change in SiGeC semiconductors",
  "title_text": "Provenance records predict which multi-scale conclusions a foundation-potential release can change in SiGeC semiconductors",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/afctrbnh65wxh",
  "venue": "ChemRxiv",
  "versions": []
}