{
  "abstract": "Crystal structure prediction (CSP) is emerging as a powerful method for the computational design of metal–organic frameworks (MOFs). In this article we employ CSP to perform high-throughput exploration of the crystal energy landscape of zinc imidazolate (ZnIm2). As the most polymorphic member of the zeolitic imidazolate framework (ZIF) family, ZnIm2 has at least 24 reported structural and topological forms, and new polymorphs are still being regularly discovered. With the aid of custom-trained machine-learned interatomic potentials (MLIPs) we have performed a high-throughput sampling of over 3 million randomly generated crystal packing arrangements and identified 9609 energy minima characterized by 1484 network topologies, including 855 topologies that have not been reported before. Of the experimentally reported structures of ZnIm2 falling within the search boundaries, all but one were ultimately matched with the predicted structures, demonstrating the power of the CSP method in sampling experimentally relevant ZIF structures. Finally, through a combination of topological analysis and density and porosity considerations, we have identified a set of structures representing promising targets for future experimental screening, as well as demonstrated how structures of mechanochemically synthesized MOFs could be identified via matching experimental powder diffraction patterns with simulated patterns from the predicted structures.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Chemistry, University of Warsaw Warsaw 02-093 Poland",
    "Division of Physical Chemistry, Ruđer Bošković Institute Zagreb 10000 Croatia",
    "School of Metallurgy and Materials, University of Birmingham Elms Road, Edgbaston Birmingham B15 2TT UK",
    "School of Chemistry and Chemical Engineering, University of Southampton Southampton SO17 1BJ UK",
    "Symmetric Group 71-75 Shelton Street, Covent Garden London WC2H 9JQ UK"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Yizhi Xu",
      "orcid": "0000-0001-7499-507X"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Jordan Dorrell",
      "orcid": "0000-0003-0043-0222"
    },
    {
      "affs": [
        1
      ],
      "name": "Katarina Lisac",
      "orcid": "0000-0002-6497-5937"
    },
    {
      "affs": [
        1
      ],
      "name": "Ivana Brekalo",
      "orcid": "0000-0002-0309-5610"
    },
    {
      "affs": [
        4
      ],
      "name": "James P. Darby",
      "orcid": "0000-0002-3365-599X"
    },
    {
      "affs": [
        2
      ],
      "name": "Andrew J. Morris",
      "orcid": "0000-0001-7453-5698"
    },
    {
      "affs": [
        0
      ],
      "name": "Mihails Arhangelskis",
      "orcid": "0000-0003-1150-3108"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7ndtf053zczc0.bib",
  "cited_by": 0,
  "doi": "10.1039/d6sc04307k",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13584093/",
  "id": "7ndtf053zczc0",
  "issns": [
    "2041-6520",
    "2041-6539"
  ],
  "landing_url": "https://doi.org/10.1039/d6sc04307k",
  "language": "en",
  "licence": "CC-BY-3.0",
  "licence_label": "CC BY 3.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/3.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Chemistry"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7120127614",
  "pdf_url": null,
  "pmcid": "PMC13584093",
  "pmid": "42756710",
  "published": "2026-09-04",
  "publisher": "Royal Society of Chemistry",
  "ris": "https://research.draconitic.com/paper/7ndtf053zczc0.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:23:30Z",
      "native_id": "PMC13584093",
      "source": "pmc",
      "url": "https://doi.org/10.1039/d6sc04307k"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Hierarchical crystal structure prediction of zeolitic imidazolate frameworks using DFT and machine-learned interatomic potentials",
  "title_text": "Hierarchical crystal structure prediction of zeolitic imidazolate frameworks using DFT and machine-learned interatomic potentials",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:20:58Z",
  "url": "https://research.draconitic.com/paper/7ndtf053zczc0",
  "venue": "Chemical Science",
  "versions": []
}