{
  "abstract": "Zeolitic Imidazolate Frameworks (ZIFs) exhibit remarkable structural flexibility that governs many of their functional properties, yet accurately describing their dynamic behavior remains a significant computational challenge. Classical force fields often fail to capture framework flexibility, whereas first-principles methods are prohibitively expensive. In this work, high-fidelity machine learning potentials (MLPs) are first developed for ZIF-8 using the equivariant Allegro architecture. To reduce the cost associated with the development of MLPs, an active-learning-inspired structural selection strategy based on the smooth overlap of atomic positions (SOAP) is implemented, allowing DFT-level accuracy to be achieved with fewer training configurations. Benchmarking against currently available models demonstrates that, while those models also offer reasonable energy predictions, their force errors remain 1 order of magnitude larger than the specialized MLP developed herein with a force mean absolute error of as small as 0.016 eV/Å. Building upon these findings, a transferable MLP spanning diverse Zn-based ZIFs is further constructed. Moreover, a hierarchical transfer learning workflow is also established to enable rapid adaptation to unseen topologies with improved accuracy using only a limited number of additional configurations. As a demonstration, MD simulations powered by the developed MLP successfully capture methane-induced gate-opening in ZIF-8, resolving the structural transition at subnanosecond resolution. Overall, this work provides a transferable, DFT-accurate machine learning force field for Zn-based ZIFs.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemical Engineering, National Taiwan University, Taipei10617, Taiwan."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Shang-Wei Lin",
      "orcid": "0000-0001-7424-0750"
    },
    {
      "affs": [
        0
      ],
      "name": "Yen-Yung Wu"
    },
    {
      "affs": [
        0
      ],
      "name": "Li-Chiang Lin",
      "orcid": "0000-0002-2821-9501"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0em7kttt0g3sy.bib",
  "cited_by": 0,
  "doi": "10.1021/acs.jctc.6c01271",
  "fulltext_url": "https://europepmc.org/articles/PMC13671164",
  "id": "0em7kttt0g3sy",
  "issns": [
    "1549-9618",
    "1549-9626"
  ],
  "landing_url": "https://europepmc.org/article/MED/42917293",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7214896237",
  "pdf_url": "https://europepmc.org/articles/PMC13671164?pdf=render",
  "pmcid": "PMC13671164",
  "pmid": "42917293",
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0em7kttt0g3sy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:36Z",
      "native_id": "MED:42917293",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42917293"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Efficient and Transferable Machine Learning Potentials for Zn-Coordinated Zeolitic Imidazolate Frameworks Through Structural Diversity Sampling and Transfer Learning.",
  "title_text": "Efficient and Transferable Machine Learning Potentials for Zn-Coordinated Zeolitic Imidazolate Frameworks Through Structural Diversity Sampling and Transfer Learning.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:36Z",
  "url": "https://research.draconitic.com/paper/0em7kttt0g3sy",
  "venue": "Journal of chemical theory and computation",
  "versions": []
}