{
  "abstract": "In this work, we present a systematic approach to predicting the structures of three sodiated aluminosilicate zeolites (Chabazite, Mordenite and RHO) by combining an accurate neural network potential with a custom basin-hopping algorithm. We show that the low energy, thermally accessible configurations are rare in comparison to the number of possible structures, accounting for a few percent of viable structures. For Chabazite and Mordenite, it is found that there is a tendency for aluminium to preferentially aggregate with 2 or 3 silicon atoms separating aluminium atoms with a strong disincentive to form Al–O–Si–O–Al linkages. However, clear and contrary topological preferences are observed in RHO where Al–O–Si–O–Al linkages are strongly favoured at moderate Al concentrations. Crucially, randomly generated or non-basin hopping approaches to structure generation results in configurations that are highly metastable and activated with respect to minimum energy structures. Using such activated structures for modelling catalysis processes could potentially yield qualitatively incorrect predictions of reactivity and transport properties.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemistry, University College London 20 Gordon Street London WC1H 0AJ UK",
    "Department of Physical and Macromolecular Chemistry, Faculty of Science, Charles University in Prague 12483 Czech Republic",
    "Johnson Matthey Technology Centre Sonning Common, Blounts Ct Rd, Sonning Common, Reading RG4 9NH UK"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Oscar Bengtsson"
    },
    {
      "affs": [
        0
      ],
      "name": "Daniel Hewitt"
    },
    {
      "affs": [
        1
      ],
      "name": "Indranil Saha"
    },
    {
      "affs": [
        2
      ],
      "name": "Misbah Sarwar",
      "orcid": "0000-0002-6249-796X"
    },
    {
      "affs": [
        2
      ],
      "name": "Nicola Collis"
    },
    {
      "affs": [
        1
      ],
      "name": "Andreas Erlebach",
      "orcid": "0000-0003-3261-6443"
    },
    {
      "affs": [
        1
      ],
      "name": "Lukáš Grajciar",
      "orcid": "0000-0001-9464-7769"
    },
    {
      "affs": [
        1
      ],
      "name": "Christopher J. Heard",
      "orcid": "0000-0001-8034-6121"
    },
    {
      "affs": [
        0
      ],
      "name": "Ben Slater",
      "orcid": "0000-0001-9738-5848"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f3qxk68bxztej.bib",
  "cited_by": 0,
  "doi": "10.1039/d6dd00356g",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13721988/?tool=EBI",
  "id": "f3qxk68bxztej",
  "issns": [
    "2635-098X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13721988",
  "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": [
    [
      "heading",
      "Chemistry"
    ],
    [
      "openalex",
      "1604|Zeolite Catalysis and Synthesis"
    ]
  ],
  "openalex": "W7127376962",
  "pdf_url": null,
  "pmcid": "PMC13721988",
  "pmid": "42965125",
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f3qxk68bxztej.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:36Z",
      "native_id": "PMC:PMC13721988",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13721988"
    },
    {
      "first_seen": "2026-11-03T12:22:35Z",
      "native_id": "PMC13721988",
      "source": "pmc",
      "url": "https://doi.org/10.1039/d6dd00356g"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1604|Zeolite Catalysis and Synthesis",
      "subfield": "inorganic"
    }
  ],
  "title": "Prediction of aluminium distributions in zeolites using basin-hopping and machine learning potentials",
  "title_text": "Prediction of aluminium distributions in zeolites using basin-hopping and machine learning potentials",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:22:35Z",
  "url": "https://research.draconitic.com/paper/f3qxk68bxztej",
  "venue": "Digital discovery",
  "versions": []
}