Machine Learning Assisted Study of Early-Stage MOF Self-Assembly: Structural Characterization and Multi-Scale Modeling

Shukla, Rishabh

Machine Learning Assisted Study of Early-Stage MOF Self-Assembly: Structural Characterization and Multi-Scale Modeling

dc.contributor.advisor	Kusalik, Peter G.
dc.contributor.author	Shukla, Rishabh
dc.contributor.committeemember	Kusalik, Peter G.
dc.contributor.committeemember	MacCallum, Justin Laine
dc.contributor.committeemember	Shimizu, George Kisa Hayashi
dc.contributor.committeemember	Salahub, Dennis R.
dc.contributor.committeemember	Woo, Tom K.
dc.date	2024-05
dc.date.accessioned	2024-01-29T18:36:12Z
dc.date.available	2024-01-29T18:36:12Z
dc.date.issued	2024-01-25
dc.description.abstract	Metal-Organic Frameworks (MOFs) are an important class of materials with a broad range of applications (e.g., gas storage and catalysis). While a great number of MOF compounds are reported in the literature, where these studies have described a multitude of ways of synthesizing MOFs, there have been few studies probing the self-assembly process. Mechanisms of MOF self-assembly are amenable to Molecular Dynamic (MD) simulations, although there have been very few such studies to date, and even they suffer from unreasonable assumptions or fall short due to the limited timescales available to all-atom simulations. To allow simulations to reach much longer timescales, this thesis project looks to find an optimal way to develop effective potentials. The all-atom molecular interactions have been redefined so that the explicit solvent molecules could be omitted from the system without losing the solvent effect on the self-assembly processes. Since a large majority of atoms in the explicit system are the solvent molecules, we expect the effective potentials would result in a dramatic acceleration of the simulations. The complexity of the potential space makes the optimization slow and sensitive to small perturbations. We have explored the challenges associated with the iterative optimization of effective potentials and worked especially on the transferability of these potentials from a small MOF-like system to an ever-evolving environment in MOF self-assembly. Another important aspect of the project is to characterize the structures seen in the self-assembly process and to quantify the structural order, which can be further used to bias the simulations and further speed up the sampling, thus bridging the gap between experimental and computational studies.
dc.identifier.citation	Shukla, R. (2024). Machine learning assisted study of early-stage MOF self-assembly: structural characterization and multi-scale modeling (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/118114
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Metal-organic framework
dc.subject	Self-assembly
dc.subject	Molecular simulation
dc.subject	Multi-scale modeling
dc.subject	Machine learning
dc.subject	Effective potentials
dc.subject	Particle swarm optimization
dc.subject.classification	Chemistry--Physical
dc.title	Machine Learning Assisted Study of Early-Stage MOF Self-Assembly: Structural Characterization and Multi-Scale Modeling
dc.type	doctoral thesis
thesis.degree.discipline	Chemistry
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.