Abstract: A surrogate predictive model for estimating the cationic site occupancy in faujasite-type (FAU) zeolites based on their chemical composition is presented. First, a reference experimental database containing structural data for 108 dehydrated FAU zeolites was compiled from literature sources. A quantitative criterion was introduced to assess the applicability of models predicting cationic site occupancy in reference zeolite frameworks. The canonical replica-exchange Monte Carlo method was used to generate a training data set. The best agreement between the simulated and experimental cationic site occupancies was achieved using a custom zeolite framework model with randomly distributed Al atoms and an extended DFT/CC-derived force field model. A set of descriptors was developed to translate the chemical composition of zeolite adsorbents into numerical parameters for modeling. The machine-learning surrogate model was trained on a simulation-derived database containing cation site occupancies for 250 monocationic and 3962 bicationic FAU zeolites generated using replica-exchange Monte Carlo simulations. Since surrogate predictive models are not limited by a set of force field parameters, they can be used to predict the cationic site occupancies in zeolites for a wide range of cations. The predictive ability of the surrogate model was demonstrated for a set of FAU zeolite frameworks from the reference database.

Cite: Grenev I.V. , Bobkov M.E. , Ivanov A.D. , Uliankina A.I. , Shubin A.A.
Machine Learning-Based Predicting of the Equilibrium Cation Distribution in Faujasite-Type Zeolites
Materials Chemistry and Physics. 2026. V.349. NPart 2. 131853 :1-11. DOI: 10.1016/j.matchemphys.2025.131853

Dates:

Submitted:	Aug 5, 2025
Accepted:	Nov 26, 2025
Published online:	Nov 29, 2025
Published print:	Feb 1, 2026

Identifiers: No identifiers

Citing: Пока нет цитирований

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