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dc.contributor.advisorKaxiras, Efthimios
dc.contributor.authorHoyt, Robert
dc.date.accessioned2019-08-08T13:17:39Z
dash.embargo.terms2019-05-01
dc.date.created2018-05
dc.date.issued2018-05-08
dc.date.submitted2018
dc.identifier.citationHoyt, Robert. 2018. Understanding Catalysts With Density Functional Theory and Machine Learning. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41129179*
dc.description.abstractCatalysts are the cornerstone of the chemicals industry, whose products are used in nearly all human endeavors. At the core of catalysis lies the intricate relationship between their atoms and electrons, where quantum mechanics dictates interactions with reactants, products, and electromagnetic fields. This it the "electronic structure" of catalysts, and studying this structure provides deep insight into the understanding and design of novel catalytic materials. This thesis focuses on understanding a small subset of promising heterogeneous catalytic systems using density functional theory (DFT), from oxygen evolution over polyiodide-doped graphene to the nonadiabatic dissociation of hydrogen over Cu nanoclusters. Some of these studies emphasize the importance of nonadiabatic behavior, especially magnetization transitions in Cu nanoclusters upon hydrogen dissociation. Further insights into catalytic properties can be obtained by comparing DFT calculations to corresponding machine learning predictions. For example, differences between DFT and empirical data-driven kernels highlight important discontinuous quantum mechanical effects in H adsorption on dilute Ag alloys. The studies presented here are examples of how detailed electronic structure calculations can be used to develop a deeper understanding of catalysts and how they might be improved.
dc.description.sponsorshipPhysics
dc.format.mimetypeapplication/pdf
dc.language.isoen
dash.licenseLAA
dc.subjectdensity functional theory
dc.subjectelectronic structure
dc.subjectcatalysis
dc.subjectcomputational chemistry
dc.subjectcomputational physics
dc.subjectchemical physics
dc.subjectmachine learning
dc.subjectdata science
dc.subjectmaterials informatics
dc.subjectcheminformatics
dc.subjecttransition state theory
dc.subject
dc.titleUnderstanding Catalysts With Density Functional Theory and Machine Learning
dc.typeThesis or Dissertation
dash.depositing.authorHoyt, Robert
dash.embargo.until2019-05-01
dc.date.available2019-08-08T13:17:39Z
thesis.degree.date2018
thesis.degree.grantorGraduate School of Arts & Sciences
thesis.degree.grantorGraduate School of Arts & Sciences
thesis.degree.levelDoctoral
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
thesis.degree.nameDoctor of Philosophy
dc.contributor.committeeMemberFriend, Cynthia
dc.contributor.committeeMemberKim, Philip
dc.type.materialtext
thesis.degree.departmentPhysics
thesis.degree.departmentPhysics
dash.identifier.vireo
dash.author.emailhoyt.robert@gmail.com


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