{
  "abstract": "Locating competing reaction pathways is difficult when potential-energy gradients are unavailable. Here we present tReax\\_expedition, an energy-only approach combining coupled Monte Carlo exploration, stochastic quench labelling and restrained continuation. Exploration identifies connected states, while continuation resolves candidate channels and records sampled chain maxima. Single-search benchmarks on three two-dimensional potentials recovered an intermediate and competing routes without transition-state guesses. For eight of nine gas-phase reactions on GFN2-xTB, sampled chain maxima differed from independently refined saddle energies by less than 0.76~kJ\\,mol$^{-1}$. Alanine dipeptide instead revealed incomplete conformational relaxation, showing that route connectivity can persist despite structural and energetic errors. Neural committor learning and local straddling-pair sampling connect the resulting paths to transition-region sampling, although molecular shooting diagnostics indicate limited committor calibration. These results show how energy-only exploration and continuation recover candidate pathways, while distinguishing route connectivity from geometric localization, barrier resolution and finite-temperature transition statistics.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.03699",
  "author_total": 1,
  "authors": [
    {
      "name": "Chien-Chai Chang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f771mh5gwv1rp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.03699",
  "fulltext_url": null,
  "id": "f771mh5gwv1rp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.03699",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.chem-ph"
    ],
    [
      "arxiv",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.03699",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f771mh5gwv1rp.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:25Z",
      "native_id": "2611.03699",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.03699"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    },
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "All Paths All at Once: A Quantum Monte Carlo and Neural Committor Approach to Reaction Pathway Search",
  "title_text": "All Paths All at Once: A Quantum Monte Carlo and Neural Committor Approach to Reaction Pathway Search",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:39Z",
  "url": "https://research.draconitic.com/paper/f771mh5gwv1rp",
  "venue": "arXiv",
  "versions": []
}