{
  "abstract": "Dealuminated HY (USY) zeolites are of major industrial catalytic importance, and the defects they contain, such as extraframework aluminum (EFAL) and silanol groups, strongly influence the behaviour of adsorbed species. Here, we show that reactive neural network-based machine learned interatomic potentials (MLIPs) provide a substantial improvement in the simulation of water diffusion in these catalysts. They yield markedly improved agreement with quasielastic neutron scattering (QENS) measurements of mobility for models of intact framework regions, refined further once EFAL and silanol defects are included explicitly, capturing the additional hindrance these sites impose on water motion. The ability of MLIPs to generate and stabilise realistic defect structures constitutes a step change in the molecular level and nanoscale modelling of industrial zeolites. This combined capability enables the quantitative investigation of defect-specific effects on nanoscale diffusion and provides a new approach for accurately modelling transport in industrially relevant zeolite catalysts.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Physical and Macromolecular Chemistry, Charles University, Hlavova 8, Prague, Czech Republic, 128 43",
    "Department of Chemistry",
    "University of Bath",
    "Institute Laue Langevin"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Indranil Saha"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Alexander James Porter"
    },
    {
      "affs": [
        0
      ],
      "name": "Andreas Erlebach"
    },
    {
      "affs": [
        3
      ],
      "name": "Jacques Ollivier"
    },
    {
      "affs": [
        0
      ],
      "name": "Christopher J. Heard"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Alexander James O’Malley"
    },
    {
      "affs": [
        0
      ],
      "name": "Lukáš Grajciar",
      "orcid": "0000-0001-9464-7769"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bxfsv5pf5ft98.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010947/v1",
  "fulltext_url": null,
  "id": "bxfsv5pf5ft98",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010947/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|Zeolite Catalysis and Synthesis"
    ]
  ],
  "openalex": "W7223324583",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010947/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/bxfsv5pf5ft98.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.26434/chemrxiv.15010947",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010947/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1604|Zeolite Catalysis and Synthesis",
      "subfield": "inorganic"
    }
  ],
  "title": "Reactive Machine-Learned Potentials Improve Predictions of Water Diffusion Behaviour in Defective Industrial Zeolite HY",
  "title_text": "Reactive Machine-Learned Potentials Improve Predictions of Water Diffusion Behaviour in Defective Industrial Zeolite HY",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/bxfsv5pf5ft98",
  "venue": "ChemRxiv",
  "versions": []
}