Molecular Simulation of Zeolites by Machine-Learned Potentials

Zeolites are a class of minerals frequently used in catalysis, gas separation, and ion exchange. Zeolites exhibit significant variability in the morphology and dimensions of their pores and channels, which directly influences their suitability for specific applications depending on the molecules intended for incorporation. By systematically screening a comprehensive array of hypothetical zeolite structures to identify those that are synthesizable, we aim to expand the pool of candidate zeolites. This expanded database will enable the selection of more tailored zeolites for particular applications, thereby enhancing overall efficiency. To screen these structures, it is essential to accurately model their thermodynamic stability. Molecular dynamics (MD) simulations are a powerful tool for this, but the choice of force field plays a critical role. Variations in atomic positions and framework distortions caused by different force fields introduce discrepancies that impact the accuracy of energy calculations. Ab initio molecular dynamics avoids these inaccuracies by computing interatomic forces from electronic structure calculations, though these calculations are very computationally expensive which limits the time scale of simulations. Alternatively, machine learning can be used to train a model on ab initio calculations to run simulations at longer time scales and at near ab initio accuracies. The first objective of this study is to produce a machine learned potential to simulate amorphous and crystalline silica at time scales previously unavailable for simulations with near ab initio accuracy. The second work will focus on models trained using more accurate meta-GGA level DFT calculations for silica, while the final works will focus on investigating the effects of ion hydration radius and structure directing agents on zeolite stability.

Name thesis:degree_name

Doctor of Philosophy

Discipline thesis:degree_discipline

Chemical Engineering

Grantor

University of Houston

Year dc:date.issued

2025

Author dc:creator

• Bourji, Hadi

Advisor dc:contributor.advisor

• Palmer, Jeremy C.

Committee members dc:contributor.committeemember

• Brgoch, Jakoah
• Zerze, Gül H
• Rimer, Jeffrey D.
• Canepa, Pieremanuele

• Chemical engineering

Language dc:language.iso

English

Handle dc:identifier.uri

https://hdl.handle.net/10657/20709

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/20709