{
  "abstract": "We introduce quantum Monte Carlo (QMC)-trained multi-state machine-learned (ML) force fields for nonadiabatic excited-state dynamics, targeting photochemical processes in which the electronic character changes along the reaction path and a consistent correlated description is required. In this framework, variational Monte Carlo wave functions combine compact selected configuration-interaction expansions with a Jastrow factor that explicitly accounts for dynamical correlation, while neural networks convert the stochastic QMC data into smooth potential energy surfaces for large surface-hopping ensembles. We apply this approach to azomethane, a demanding test case involving torsional relaxation through conical-intersection regions and C--N bond dissociation on the hot ground state. Benchmark calculations support the accuracy of the QMC reference data and show robust force convergence across isomerization and dissociation geometries. The QMC-trained dynamics preserves the expected photoisomerization mechanism, strongly reduces the excessive C--N breaking obtained with complete active space self-consistent field, and predicts a small but non-negligible prompt dissociation component after internal conversion, with a timescale consistent with femtosecond-resolved mass-spectrometry experiments. These results establish QMC-ML as a practical route to nonadiabatic photochemical dynamics with accurate wave-function reference data.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.16129",
  "author_total": 3,
  "authors": [
    {
      "name": "Alfonso Annarelli"
    },
    {
      "name": "Emiel Slootman"
    },
    {
      "name": "Claudia Filippi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/epb2aapxsceyh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.16129",
  "fulltext_url": null,
  "id": "epb2aapxsceyh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.16129",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.chem-ph"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2607.16129",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/epb2aapxsceyh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:31Z",
      "native_id": "2607.16129",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.16129"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    }
  ],
  "title": "Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test",
  "title_text": "Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:31Z",
  "url": "https://research.draconitic.com/paper/epb2aapxsceyh",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1021/acs.jctc.6c01442",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}