{
  "abstract": "Fluxionality, or the population of and interconversion between multiple structures, has long been suggested to be relevant for understanding the physical and catalytic behavior of sub-nanoscale metal clusters on an oxide support. Much of this treatment has, however, been thermodynamic, neglecting direct exploration of the timescales for interconversion between di!erent isomers. Previously, we exploited machine-learned interatomic potentials, combined with automatic process exploration (MLIP-APE) to identify isomerization networks for MgO-supported Pd clusters, capturing all barriers for cluster restructuring. In the current work, we demonstrate a new analysis approach based on Markov State Modeling (MSM) which captures the complex contribution of multiple timescales on the overall fluxional dynamic behavior over a range of cluster sizes. This overall fluxional behavior can then be classified between the extremes of highly dynamic “labile” clusters, to “rigid” clusters which exhibit much less dynamic behavior. These dynamics are of particular interest when using fluxional clusters for catalysis, as these dynamics could influence the observed catalytic reactions. Ultimately, this work demonstrates a rigorous and general approach to investigate the dynamics of reaction networks of well-defined states.",
  "abstract_shown": true,
  "affiliations": [
    "Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, Berlin, Germany, 14195",
    "LAAS-CNRS, Université de Toulouse, 7 Av. du Colonel Roche, Toulouse, France, 31400"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Patricia Poths",
      "orcid": "0000-0003-4193-526X"
    },
    {
      "affs": [
        1
      ],
      "name": "Feliks Nüske"
    },
    {
      "affs": [
        0
      ],
      "name": "Sebastian Matera"
    },
    {
      "affs": [
        0
      ],
      "name": "Karsten Reuter"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9dyrwbx4ez5yy.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010582/v1",
  "fulltext_url": null,
  "id": "9dyrwbx4ez5yy",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010582/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": "W7222423382",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/9dyrwbx4ez5yy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15010582",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010582/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Describing the Fluxional Dynamics of Sub-nanoscale Clusters with Markov State Modeling",
  "title_text": "Describing the Fluxional Dynamics of Sub-nanoscale Clusters with Markov State Modeling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/9dyrwbx4ez5yy",
  "venue": "ChemRxiv",
  "versions": []
}