{
  "abstract": "Chemical kinetics determines molecular stability, reaction selectivity and the evolution of reacting mixtures, but discovering the underlying reaction networks remains challenging. Here we introduce ReactNexus, a generative framework for kinetic-guided network exploration starting solely from reactant graphs. Generative models propose transition states and predict the full reaction trajectories connecting reactants and products, while calculations using a machine-learned interatomic potential verify connectivity and evaluate electronic barriers. On an in-distribution benchmark of 10,000 generated candidates, 80.8% connect to the input reactant after density functional theory (DFT) optimization and intrinsic reaction coordinate verification. The resulting networks uncover lower-barrier cooperative pathways and competition between catalyst release and catalyst-bound branches. In the examined carbocation cascades, coverage of known product precursors saturates after limited expansion and distinct entries converge onto shared intermediates, indicating a compact accessible reaction space. Network-derived candidate structures further assist mass-spectral interpretation of a complex nucleophilic aromatic substitution reaction. Together, these results establish a generative approach to reaction-network discovery and mechanistic analysis without predefined products or reaction templates.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.38236",
  "author_total": 19,
  "authors": [
    {
      "name": "Yunhong Han"
    },
    {
      "name": "Gaopeng Ren"
    },
    {
      "name": "Yan Liu"
    },
    {
      "name": "Ronghua Zhang"
    },
    {
      "name": "Huangjiang Huang"
    },
    {
      "name": "Ruiqi Zhang"
    },
    {
      "name": "Zhaoxi Yu"
    },
    {
      "name": "Chuncheng Liu"
    },
    {
      "name": "Zhao Li"
    },
    {
      "name": "Xiaohan Lin"
    },
    {
      "name": "Jiaxu Wang"
    },
    {
      "name": "Yuxuan Qin"
    },
    {
      "name": "Meijie Wang"
    },
    {
      "name": "Guoshun Mei"
    },
    {
      "name": "Doudou Wang"
    },
    {
      "name": "Taoyong Cui"
    },
    {
      "name": "Haojun Jia"
    },
    {
      "name": "Chenru Duan"
    },
    {
      "name": "Qiyuan Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/100cyz79eggqz.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.38236",
  "fulltext_url": null,
  "id": "100cyz79eggqz",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.38236",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.chem-ph"
    ],
    [
      "arxiv",
      "q-bio.MN"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.38236",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/100cyz79eggqz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:23Z",
      "native_id": "2610.38236",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.38236"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.MN",
      "subfield": "systems-synthetic"
    },
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "Generative discovery of chemical reaction networks",
  "title_text": "Generative discovery of chemical reaction networks",
  "type": "preprint",
  "updated_at": "2026-11-01T12:20:56Z",
  "url": "https://research.draconitic.com/paper/100cyz79eggqz",
  "venue": "arXiv",
  "versions": []
}