{
  "abstract": "AI-assisted study spanning molecular stability, the Born–Oppenheimer approximation, chemical bonding, density functional theory, electron correlation, reaction dynamics, thermodynamics, kinetics, and transport. Across the available referee reports, all ten chapters were recommended for acceptance after minor revision within the assessment rubric used in the review project, and no confirmed substantive error was identified that invalidated a principal scientific conclusion. The main value of the project does not lie in claiming ten new fundamental theorems. Rather, the chapters repeatedly make explicit the mathematical steps separating one level of description from another: stability from binding, exact theory from approximation, finite-system calculation from an asymptotic limit, microscopic dynamics from effective kinetics, and conservation laws from macroscopic transport. The review record therefore suggests that AI can assist in constructing technically sophisticated and auditable theoretical arguments. It also motivates, but does not by itself establish, a broader hypothesis: as scientific generation becomes easier, independent verification may become an increasingly important bottleneck.",
  "abstract_shown": true,
  "affiliations": [
    "Polish–Japanese Academy of Information Technology",
    "Chair of Theoretical Physics of Naturally Intelligent Systems (NIS",
    "European Union"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mikołaj Sienicki"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Krzysztof Sienicki",
      "orcid": "0009-0008-3410-5472"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/drn3036e1afzs.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008284/v1",
  "fulltext_url": null,
  "id": "drn3036e1afzs",
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  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008284/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",
      "3107|Advanced Chemical Physics Studies"
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  "openalex": "W7208717097",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008284/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/drn3036e1afzs.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008284",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008284/v1"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3107|Advanced Chemical Physics Studies",
      "subfield": "amo"
    }
  ],
  "title": "A Human Audit of AI-Generated Theoretical Chemistry: Lessons from Ten Chapter-Level Peer Reviews",
  "title_text": "A Human Audit of AI-Generated Theoretical Chemistry: Lessons from Ten Chapter-Level Peer Reviews",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/drn3036e1afzs",
  "venue": "ChemRxiv",
  "versions": []
}