{
  "abstract": "Geometry optimization is a major cost in many quantum-chemical workflows: each optimization step requires one force evaluation, and at the density-functional level that evaluation dominates the wall time. Research in this area has produced a broad range of optimization methods, and we ask whether a language model can improve on the best of them through autoresearch. An agent rewrites the optimizer itself to minimize force-call counts, restrained by two admission gates that reject premature stopping and improvements that do not generalize to unseen molecules. Starting from Sella, the fastest open-source optimizer available, the search produces AutoSella, a family of two optimizers. Both of them deliver consistent force-call reductions relative to Sella across held-out molecular benchmarks and potentials not used during the search. Most notably, at the r2SCAN-3c DFT level, the best variant requires only 40.2-77.2% of Sella's force calls while achieving the same energy reduction, even though agent used no DFT gradients.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.06577",
  "author_total": 7,
  "authors": [
    {
      "name": "Artem Tsypin"
    },
    {
      "name": "Vladimir Deshchenya"
    },
    {
      "name": "Kuzma Khrabrov"
    },
    {
      "name": "Denis Potapov"
    },
    {
      "name": "Maxim Radchenko"
    },
    {
      "name": "Artur Kadurin"
    },
    {
      "name": "Michael G. Medvedev"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2y05qan980sst.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.06577",
  "fulltext_url": null,
  "id": "2y05qan980sst",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.06577",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.chem-ph"
    ],
    [
      "openalex",
      "3107|Advanced Chemical Physics Studies"
    ]
  ],
  "openalex": "W7220696933",
  "pdf_url": "https://arxiv.org/pdf/2610.06577",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2y05qan980sst.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:37Z",
      "native_id": "2610.06577",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.06577"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "Optimizing the Optimizer: Language Models Discover Faster Molecular Relaxation Algorithms",
  "title_text": "Optimizing the Optimizer: Language Models Discover Faster Molecular Relaxation Algorithms",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:37Z",
  "url": "https://research.draconitic.com/paper/2y05qan980sst",
  "venue": "arXiv",
  "versions": []
}