{
  "abstract": "Autonomousmaterials laboratories revise conclusions fromevidence sources (calculations,machine-learned potentials, databases, furnace runs) whose errors are known in kind but not in size. These revisions are self-reported, so a reader cannot check that no batch was dropped or that a retraction removed what it claims; verifiable unlearning covers only erasure from trained machinelearning models. The aim is a record that lets anyone check every revision without trusting the campaign. Full checkability is hypothesised to be affordable when beliefs are compact symbolic models, whose clean retrain takes seconds. Designs are hashed onto an append-only campaign record before runs start, raw outputs are committed before analysis, and revisions are verified by deterministic re-execution. Four pre-registered campaigns on heat transport in SiGe and SiGeC semiconductors (molecular dynamics, machine-learned relaxations, phonon transport, an experimental database) test the scheme. The record keeps failed checks, refuted predictions and set-aside evidence in view, and an independent verifier re-checks it in about three seconds. It meets pharmaceutical data-integrity rules; separating a measurement’s influence on fitted laws from the measurement reconciles the European right to erasure with mandatory retention. Materials claims, such as howmuch germanium carbon can replace, can thus be adopted and extended on an inspectable record.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Kinston Ackölf",
      "orcid": "0000-0002-5579-4878"
    },
    {
      "name": "Taylor D. Sparks"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8kss7gsbybrfe.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010668/v1",
  "fulltext_url": null,
  "id": "8kss7gsbybrfe",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010668/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": "W7222478302",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010668/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/8kss7gsbybrfe.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15010668",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010668/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Hash-committed pre-registration and exact unlearning make belief revisions in autonomous SiGeC semiconductor campaigns checkable",
  "title_text": "Hash-committed pre-registration and exact unlearning make belief revisions in autonomous SiGeC semiconductor campaigns checkable",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/8kss7gsbybrfe",
  "venue": "ChemRxiv",
  "versions": []
}