Insights into computational methods for surface science and catalysis

dc.contributor.advisorHenkelman, Graeme
dc.contributor.committeeMemberHumphrey, Simon M
dc.contributor.committeeMemberHwang, Gyeong S.
dc.contributor.committeeMemberWebb, Lauren J.
dc.creatorCiufo, Ryan Anthony
dc.date.accessioned2022-05-13T21:51:03Z
dc.date.available2022-05-13T21:51:03Z
dc.date.created2021-12
dc.date.issued2021-12-06
dc.date.submittedDecember 2021
dc.date.updated2022-05-13T21:51:04Z
dc.description.abstractThe fundamental understanding of both the reactions at catalytic surfaces and the ways in which these surfaces change throughout a catalytic cycle and lifetime are important for both academic and industrial disciplines. To develop these understandings on complex catalytic systems, ultra-high vacuum techniques such as molecular beam studies, temperature programmed desorption, reflection-absorption infrared spectroscopy and Auger electron spectroscopy can be used to study the simplest interactions between gas molecules and surfaces. These interactions can be studied from a bottom-up approach to learn about the system in question, upon which additional complexities can be added. To parallel these experimental techniques, a number of computational methods can be used to support findings and guide new experiments. Ab-initio electronic structure calculations allow for a better understanding of adsorbate-surface interactions, while long timescale dynamic simulations provide insight into the time evolution and kinetics of catalysts and catalytic surfaces. Empirical and machine-learning guided potentials can be developed to lessen computational cost while retaining accuracies comparable to ab-initio calculations. Fitting such potentials ultimately allows for larger calculations to be performed and longer timescales to be simulated. The above methods will be applied to a number of industrially and academically relevant catalytic systems, including studying the interaction of H₂ and CO with Cobalt based Fischer-Tropsch catalysts and the interaction between hydrogen and palladium surfaces. Additionally, the development of a machine learning package to fit and use interatomic potentials will be discussed.
dc.description.departmentChemistry
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/114157
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/41060
dc.language.isoen
dc.subjectComputational
dc.subjectChemistry
dc.subjectSurface
dc.subjectScience
dc.subjectCatalysis
dc.titleInsights into computational methods for surface science and catalysis
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentChemistry
thesis.degree.disciplineChemistry
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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