{
  "abstract": "Rare transitions limit the direct simulation of molecular kinetics and complicate the design of enhanced-sampling biases that accelerate specific events without obscuring their mechanism. We develop a data-driven framework in which biased trajectory data are combined using dynamic histogram analysis and represented by either conventional grids or normalized kernel-anchor memberships to construct finite row-stochastic kinetic models. These models are used to adapt a bounded bias by directly minimizing an absorbing mean first-passage time (MFPT) objective, while reactive-current diagnostics independently assess whether accelerated trajectories preserve the reference transition mechanism. We first test the kernel-induced representation in equilibrium free-energy reconstruction and then apply the kinetic optimization framework to progressively more challenging rareevent problems, including membrane permeation, multidimensional barrier crossing, and a metastable molecular rearrangement. The benchmark calculations show that MFPT-directed biasing can efficiently promote target arrival and that soft kernel states provide a flexible alternative to rigid spatial discretization. At the same time, pathway analysis shows that faster arrival does not imply preservation of the underlying transition mechanism. A confounded high-dimensional stress test further exposes the limitations of projected kinetic models when the relevant dynamical coordinates are unresolved.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Physics and Astronomy, University College London, Gower Street, London, United Kingdom, WC1E 6BT",
    "School of Medicine, Pharmacy and Biomedical Sciences, Faculty of Science and Health",
    "University of Portsmouth"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Qingying Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Tingyue Zhou"
    },
    {
      "affs": [
        0
      ],
      "name": "Baowen Li"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Christian Jorgensen"
    },
    {
      "affs": [
        0
      ],
      "name": "Edina Rosta",
      "orcid": "0000-0002-9823-4766"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0r3tq15hddz84.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009623/v1",
  "fulltext_url": null,
  "id": "0r3tq15hddz84",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009623/v1",
  "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": "W7214708857",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/0r3tq15hddz84.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009623",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009623/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1606|Advanced Physical and Chemical Molecular Interactions",
      "subfield": "physical"
    }
  ],
  "title": "Mean First-Passage-Time-Directed Bias Optimization with Gaussian-Kernel Markov Models",
  "title_text": "Mean First-Passage-Time-Directed Bias Optimization with Gaussian-Kernel Markov Models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/0r3tq15hddz84",
  "venue": "ChemRxiv",
  "versions": []
}