{
  "abstract": "Transferable force fields enable routine molecular dynamics simulations across broad chemical space, but finite transferable parameter sets may not describe unusual chemical environments with uniform accuracy.Here, we present ParmFit, an automated preciseforce-field parameterization framework within MAPLE that uses machine-learning interatomic potentials to efficiently construct and refine AMBER/GAFF-compatible parameters.ParmFit extends this strategy across small molecules, chemically modified biomolecular residues, and metal coordination environments while selectively retaining compatible force field terms.In a challenging subset of FreeSolv, UMA-based precise parameters reduced the average hydration free-energy prediction error by more than 1 kcal mol -1 , while ParmFit-generated models of covalently modified proteins and metalloproteins maintained stable structural and coordination behavior in molecular dynamics simulations.By integrating efficient MLIP calculations, selective parameter refinement, and direct compatibility with established force-field workflows, ParmFit provides a practical route toward broader use of precise force fields in molecular simulation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States",
    "Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States",
    "Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego, CA 92093, United States"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xuchao Zhou",
      "orcid": "0009-0004-7749-1052"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Xujian Wang",
      "orcid": "0009-0004-0146-9991"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiahao Xie",
      "orcid": "0009-0004-2387-7021"
    },
    {
      "affs": [
        2
      ],
      "name": "Leshen Zhang",
      "orcid": "0009-0008-9143-6737"
    },
    {
      "affs": [
        0
      ],
      "name": "Junmei Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/133yrfgxevvaq.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008812/v1",
  "fulltext_url": null,
  "id": "133yrfgxevvaq",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008812/v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3107|Advanced Chemical Physics Studies"
    ]
  ],
  "openalex": "W7213268913",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008812/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/133yrfgxevvaq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15008812",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008812/v2"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3107|Advanced Chemical Physics Studies",
      "subfield": "amo"
    }
  ],
  "title": "Leveraging Machine Learning Interatomic Potentials into Fitting Fast and Accurate AMBER-Compatible Precise Force Field",
  "title_text": "Leveraging Machine Learning Interatomic Potentials into Fitting Fast and Accurate AMBER-Compatible Precise Force Field",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/133yrfgxevvaq",
  "venue": "ChemRxiv",
  "versions": []
}