Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 16 min ago
Peer-reviewedCC BYDigital discovery

Prediction of aluminium distributions in zeolites using basin-hopping and machine learning potentials

  • Oscar Bengtsson1
  • Daniel Hewitt1
  • Indranil Saha2
  • Misbah Sarwar3
  • Nicola Collis3
  • Andreas Erlebach2
  • Lukáš Grajciar2
  • Christopher J. Heard2
  • Ben Slater1
  1. 1Department of Chemistry, University College London 20 Gordon Street London WC1H 0AJ UK
  2. 2Department of Physical and Macromolecular Chemistry, Faculty of Science, Charles University in Prague 12483 Czech Republic
  3. 3Johnson Matthey Technology Centre Sonning Common, Blounts Ct Rd, Sonning Common, Reading RG4 9NH UK
Read full textDOICite

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.

Related

  1. Hierarchical crystal structure prediction of zeolitic imidazolate frameworks using DFT and machine-learned interatomic potentials
    Yizhi Xu, Jordan Dorrell, Katarina Lisac +4
    Chemical SciencePeer-reviewedCC BY 3.0Full textMaterials & polymersPMC
  2. Correcting the Liquid Densities of Universal Machine-Learning Interatomic Potentials Using Local-Number-Density Repulsion
    Shunsuke Mieda
    ChemRxivPreprintLicence not statedGeneral chemistryChemRxiv
  3. pdb2reaction: End-to-End Reaction-Path Elucidation from PDB Structures Using Machine-Learning Interatomic Potentials
    Takuto Ohmura, Hajime Sato, Tohru Terada
    ChemRxivPreprintLicence not statedGeneral chemistryChemRxiv
  4. Applicability Domain of Universal Machine Learning Interatomic Potentials at Solid-State Battery Interfaces
    Umang Agarwal, Mengke Li, Gopalakrishnan Sai Gautam +1
    ChemRxivPreprintLicence not statedGeneral chemistryChemRxiv
  5. How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?
    Luuk H. E. Kempen, Raffaele Cheula, Mie Andersen
    The Journal of Chemical PhysicsPeer-reviewedLicence not statedFull text9 citedMaterials & polymersOpenAlex

Cite

BibTeXRIS
BibTeX
@article{bengtsson2026prediction,
  title = {{Prediction of aluminium distributions in zeolites using basin-hopping and machine learning potentials}},
  author = {Oscar Bengtsson and Daniel Hewitt and Indranil Saha and Misbah Sarwar and Nicola Collis and Andreas Erlebach and Lukáš Grajciar and Christopher J. Heard and Ben Slater},
  journal = {Digital discovery},
  year = {2026},
  month = oct,
  doi = {10.1039/d6dd00356g},
  url = {https://europepmc.org/article/PMC/PMC13721988},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Prediction of aluminium distributions in zeolites using basin-hopping and machine learning potentials
AU  - Oscar Bengtsson
AU  - Daniel Hewitt
AU  - Indranil Saha
AU  - Misbah Sarwar
AU  - Nicola Collis
AU  - Andreas Erlebach
AU  - Lukáš Grajciar
AU  - Christopher J. Heard
AU  - Ben Slater
PY  - 2026
DA  - 2026/10/30
JO  - Digital discovery
DO  - 10.1039/d6dd00356g
UR  - https://europepmc.org/article/PMC/PMC13721988
AB  - 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.
SN  - 2635-098X
ER  -