{
  "abstract": "Machine-learned models can now propose three-dimensional transition-state (TS) geometries in seconds, but a candidate geometry is not a validated TS. The gap between a plausible structure and a mechanistically meaningful barrier—one imaginary frequency, the intended atom pair, the correct connectivity, and a consistent electronic state—remains the bottleneck in AI-assisted computational chemistry. We present a validation-first protocol that separates candidate generation from physical validation. The protocol combines (i) a chemical preflight that fixes reactive pairs, spin surfaces, connectivity, and conformers; (ii) a per-point independent scan that localizes the barrier without fragile collective scans; (iii) a constrained reference calculation that produces the Hessian; (iv) an OptTS stage that reuses that Hessian; and (v) a validation stage based on imaginary-mode localization, key bond lengths, IRC endpoints, spin diagnostics, and energy-convention checks. The protocol is implemented as a portable, open-source skill package for local ORCA/xtb and Gaussian/Slurm execution. Case studies from Ni/Fe catalysis illustrate the failure modes the protocol is designed to catch: a generative model produced candidates with 3.6–4.4 Å heavy-atom RMSD for an 84-atom broken-symmetry organometallic system, a collective scan failed three times where a per-point strategy completed 16/16 points, and a single-conformer R/S comparison would have misread a conformerdominated difference. The reported gains of multi-sampling and conformer enumeration are taken from the published benchmark, not measured here. We argue that the near-term role of AI is not to replace electronic-structure judgment but to shrink the search space before it and to make the validation layer reproducible.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Haofan Li",
      "orcid": "0000-0001-8249-7126"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8nfe0f75v7rf6.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15011128/v1",
  "fulltext_url": null,
  "id": "8nfe0f75v7rf6",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011128/v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3107|Advanced Chemical Physics Studies"
    ]
  ],
  "openalex": "W7223804400",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15011128/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/8nfe0f75v7rf6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.26434/chemrxiv.15011128",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011128/v1"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3107|Advanced Chemical Physics Studies",
      "subfield": "amo"
    }
  ],
  "title": "Beyond Candidate Generation: A Validation-First Protocol for AI-Assisted Transition-State Discovery",
  "title_text": "Beyond Candidate Generation: A Validation-First Protocol for AI-Assisted Transition-State Discovery",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/8nfe0f75v7rf6",
  "venue": "ChemRxiv",
  "versions": []
}