{
  "abstract": "Metal-organic polyhedra (MOPs) offer a modular route to porous molecular materials, but their discovery is limited by the difficulty of translating digital designs into experimentally accessible compounds. Here, we demonstrate an integrated workflow for predicting and synthesising novel MOPs in The World Avatar. To our knowledge, this is the first reticular material discovery workflow executed and persisted entirely in a shared machine-readable knowledge representation, without human-specified initial reaction conditions. This domain’s small literature corpus hinders data-driven synthesis prediction, yet its high symmetry provides exploitable structural analogies. We therefore encode auditable chemical reasoning as explicit rules rather than learning it statistically. MOP candidates are linked to synthesis knowledge, so procedures for new targets are inferred by analogy to known materials and rendered as human-readable instructions and machine-executable protocols. Candidates are assessed through hierarchical geometry optimisation, electronic-structure evaluation, crystal structure prediction, and simulated powder X-ray diffraction. We evaluate the workflow retrospectively against reported zirconium MOP syntheses and prospectively by synthesising a previously unreported Zr-EDB-MOP. Infrared spectroscopy, high-resolution mass spectrometry, and powder X-ray diffraction support formation of the targeted cage in agreement with the predicted model. This establishes a proof of concept for knowledge-integrated discovery in sparse, highly structured materials domains.",
  "abstract_shown": true,
  "affiliations": [
    "CMPG - Computational Modelling Pirmasens GmbH, Delaware Avenue 1–3, Pirmasens, Germany, 66953",
    "Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, United Kingdom, CB3 0AS",
    "School of Chemistry Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore, Singapore, 637459",
    "CARES - Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, Singapore, Singapore, 138602",
    "Institute of Physical and Theoretical Chemistry, Graz University of Technology, Stremayrgasse 9, Graz, Austria, 8010",
    "Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, MA, United States, 02139"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Simon D. Rihm",
      "orcid": "0000-0001-8342-7269"
    },
    {
      "affs": [
        1
      ],
      "name": "Patrick Butler",
      "orcid": "0000-0003-2842-836X"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Kumaran Elumalai"
    },
    {
      "affs": [
        2
      ],
      "name": "Ari Fischer"
    },
    {
      "affs": [
        2
      ],
      "name": "Pei Chong Lim"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Xu Rong",
      "orcid": "0000-0002-7562-2627"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Tej S. Choksi",
      "orcid": "0000-0002-9520-019X"
    },
    {
      "affs": [
        4
      ],
      "name": "Aleksandar Kondinski",
      "orcid": "0000-0002-0559-0172"
    },
    {
      "affs": [
        0,
        1,
        3,
        5
      ],
      "name": "Markus Kraft"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/djrmq0vqcf13n.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15007397/v1",
  "fulltext_url": null,
  "id": "djrmq0vqcf13n",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15007397/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",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7211953004",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15007397/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-13",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/djrmq0vqcf13n.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15007397",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15007397/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Prediction and synthesis of novel metal-organic polyhedra in The World Avatar",
  "title_text": "Prediction and synthesis of novel metal-organic polyhedra in The World Avatar",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/djrmq0vqcf13n",
  "venue": "ChemRxiv",
  "versions": []
}