Reactive Machine-Learned Potentials Improve Predictions of Water Diffusion Behaviour in Defective Industrial Zeolite HY
4 affiliations
- 1Department of Physical and Macromolecular Chemistry, Charles University, Hlavova 8, Prague, Czech Republic, 128 43
- 2Department of Chemistry
- 3University of Bath
- 4Institute Laue Langevin
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.
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BibTeX
@misc{saha2026reactive,
title = {{Reactive Machine-Learned Potentials Improve Predictions of Water Diffusion Behaviour in Defective Industrial Zeolite HY}},
author = {Indranil Saha and Alexander James Porter and Andreas Erlebach and Jacques Ollivier and Christopher J. Heard and Alexander James O’Malley and Lukáš Grajciar},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15010947/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010947/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Reactive Machine-Learned Potentials Improve Predictions of Water Diffusion Behaviour in Defective Industrial Zeolite HY
AU - Indranil Saha
AU - Alexander James Porter
AU - Andreas Erlebach
AU - Jacques Ollivier
AU - Christopher J. Heard
AU - Alexander James O’Malley
AU - Lukáš Grajciar
PY - 2026
DA - 2026/10/23
PB - ChemRxiv
DO - 10.26434/chemrxiv.15010947/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010947/v1
AB - 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.
ER -