{
  "abstract": "Atomistic simulations are often restricted by timescale limitations when systems become trapped in metastable energy basins. The transitions between these metastable states are rare events and they dictate the long-term evolution of the system. We present an importance sampling framework to accelerate the time scale of Langevin dynamics simulations. The framework uses a neural network parameterized importance function to bias the dynamics, enhancing the efficiency of rare-transition sampling while preserving relative probabilities between transition paths. We provide a rigorous mathematical formulation to recover the original transition rates between metastable states from the biased dynamics, and use a branching random walk algorithm to control the statistical variance of the estimated rates. We validate the framework on 2- and 14-dimensional problems, as well as the system of 7 Lennard-Jones discs (d = 14) model. We further apply it to two realistic materials systems: the diffusion of a Pt adatom on the Pt(001) surface (d = 387) and the migration of a vacancy in BCC \\alpha-iron (d = 2055). The framework provides a scalable foundation for accelerating the simulation of rare events in atomistic systems.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Mechanical Engineering",
    "Stanford University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Michael Kim",
      "orcid": "0000-0001-6806-2759"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Wei Cai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0yr250x9tszv6.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15000772/v3",
  "fulltext_url": null,
  "id": "0yr250x9tszv6",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15000772/v3",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1606|Advanced Physical and Chemical Molecular Interactions"
    ]
  ],
  "openalex": "W7221108039",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/0yr250x9tszv6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15000772",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15000772/v3"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1606|Advanced Physical and Chemical Molecular Interactions",
      "subfield": "physical"
    }
  ],
  "title": "Accelerated Langevin Dynamics Simulation via Neural Network-Driven Importance Sampling",
  "title_text": "Accelerated Langevin Dynamics Simulation via Neural Network-Driven Importance Sampling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/0yr250x9tszv6",
  "venue": "ChemRxiv",
  "versions": []
}