Developing heuristics and workflows for qm/mm simulations of ionic materials

Van Vuren, Oscar 2026. Developing heuristics and workflows for qm/mm simulations of ionic materials. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

The geometric and electronic complexity of catalytic materials necessitates using accurate, and expensive, theoretical treatments. Embedding meth ods alleviate this cost by reducing the number of atoms needing expen sive treatment; however, these models are challenging to construct and use. The development of heuristics aims to make embedding more accessible. This thesis explores automation and heuristics for QM/MM simulation setup, focusing on catalysis applications. Magnesium oxide (MgO), a rep resentative catalyst support material, was used for this purpose. QM re gion shape was found to be critical, with three shapes (radial, unit cell, SKZCAM) considered. For bulk MgO, a unit cell shaped QM region best modelled neutral and charged oxygen vacancy defects, due to the preserva tion of stoichiometry and a better representation of material structure. The heuristics for bulk simulations were applied to the (100) surface of MgO, where accurate defect formations were calculated without further parame terisation, demonstrating model transferability within the same material. Adsorption energies to the (100) surface of MgO were calculated, however a universally optimal QM region shape could not be determined. The ad sorption study demonstrates the value of problem dependent partitioning in an embedded cluster, with surface defect models overconverged for cal culating adsorption energies. Using the heuristics developed for MgO, pa rameter transferability across materials was tested using magnesium alu minium oxide (MgAl2O4) spinel, considering the neutral oxygen vacancy defect formation energy. Whilst parameters were not directly transferable, accuracy to within 30 meV of a high quality defect formation energy, calcu lated in periodic boundary conditions, was achieved without further fitting. ii A workflow to automate embedded cluster QM/MM cluster preparation using the heuristics defined in this thesis was developed and is included in the py-ChemShell software package. This work demonstrates the use of automated cluster construction proce dures and heuristics to accelerate the study of catalytically relevant materi als using embedded cluster QM/MM simulations.

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Schools > Chemistry
Date of First Compliant Deposit:	20 July 2026
Last Modified:	20 Jul 2026 10:19
URI:	https://orca.cardiff.ac.uk/id/eprint/188297

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