Describing the Fluxional Dynamics of Sub-nanoscale Clusters with Markov State Modeling
- 1Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, Berlin, Germany, 14195
- 2LAAS-CNRS, Université de Toulouse, 7 Av. du Colonel Roche, Toulouse, France, 31400
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.
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BibTeX
@misc{poths2026describing,
title = {{Describing the Fluxional Dynamics of Sub-nanoscale Clusters with Markov State Modeling}},
author = {Patricia Poths and Feliks Nüske and Sebastian Matera and Karsten Reuter},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15010582/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010582/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Describing the Fluxional Dynamics of Sub-nanoscale Clusters with Markov State Modeling
AU - Patricia Poths
AU - Feliks Nüske
AU - Sebastian Matera
AU - Karsten Reuter
PY - 2026
DA - 2026/10/16
PB - ChemRxiv
DO - 10.26434/chemrxiv.15010582/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010582/v1
AB - 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.
ER -