{
  "abstract": "Pore confinement in zirconium-based metal–organic frameworks (Zr-MOFs) is often described using the cage–window ratio (ΓCW), the largest cavity diameter divided by the pore-limiting diameter (Df), yet whether this composite coordinate carries information beyond Df alone remains unclear. We modeled CO2 uptake, heat of adsorption (HoA), and CO2/N2 selectivity for 239 Zr-containing MOFs at two pressures relevant to vacuum-swing adsorption using structure-grouped machine learning with SHAP interpretation, reaching test R2 values of 0.77–0.88. Although ΓCW correlated positively with all three targets, controlling for Df weakened these associations and reversed their sign, while the reciprocal analysis left the Df associations largely unchanged and a difference-based formulation produced no significant association. Df played different roles across the three targets, indicating that the apparent confinement effect mainly reflects the limiting aperture rather than relative cage–window architecture.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Leyli Vali",
      "orcid": "0009-0004-9438-8428"
    },
    {
      "name": "Seyed Majid Hashemianzadeh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aaefsh92vj44j.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008444/v1",
  "fulltext_url": null,
  "id": "aaefsh92vj44j",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008444/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",
      "1604|Metal-Organic Frameworks: Synthesis and Applications"
    ]
  ],
  "openalex": "W7211946230",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008444/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/aaefsh92vj44j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008444",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008444/v3"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1604|Metal-Organic Frameworks: Synthesis and Applications",
      "subfield": "inorganic"
    }
  ],
  "title": "Interpretable Machine Learning Disentangles Pore Confinement Across Multiple Zr-MOF CO2 Capture Targets",
  "title_text": "Interpretable Machine Learning Disentangles Pore Confinement Across Multiple Zr-MOF CO2 Capture Targets",
  "type": "preprint",
  "updated_at": "2026-11-05T00:20:35Z",
  "url": "https://research.draconitic.com/paper/aaefsh92vj44j",
  "venue": "ChemRxiv",
  "versions": []
}