Characterizing water-metal interfaces and machine learning potential energy surfaces

dc.contributor.advisorTamblyn, Isaac
dc.contributor.authorRyczko, Kevin
dc.date.accessioned2017-11-17T15:45:42Z
dc.date.accessioned2022-03-29T17:39:18Z
dc.date.available2017-11-17T15:45:42Z
dc.date.available2022-03-29T17:39:18Z
dc.date.issued2017-08-01
dc.degree.disciplineModelling and Computational Science
dc.degree.levelMaster of Science (MSc)
dc.description.abstractIn this thesis, we first discuss the fundamentals of ab initio electronic structure theory and density functional theory (DFT). We also discuss statistics related to computing thermodynamic averages of molecular dynamics (MD). We then use this theory to analyze and compare the structural, dynamical, and electronic properties of liquid water next to prototypical metals including platinum, graphite, and graphene. Our results are built on Born-Oppenheimer molecular dynamics (BOMD) generated using density functional theory (DFT) which explicitly include van der Waals (vdW) interactions within a first principles approach. All calculations reported use large simulation cells, allowing for an accurate treatment of the water-electrode interfaces. We have included vdW interactions through the use of the optB86b-vdW exchange correlation functional. Comparisons with the Perdew-Burke-Ernzerhof (PBE) exchange correlation functional are also shown. We find an initial peak, due to chemisorption, in the density profile of the liquid water-Pt interface not seen in the liquid water-graphite interface, liquid water-graphene interface, nor interfaces studied previously. To further investigate this chemisorption peak, we also report differences in the electronic structure of single water molecules on both Pt and graphite surfaces. We find that a covalent bond forms between the single water molecule and the platinum surface, but not between the single water molecule and the graphite surface. We also discuss the effects that defects and dopants in the graphite and graphene surfaces have on the structure and dynamics of liquid water. Lastly, we introduce artificial neural networks (ANNs), and demonstrate how they can be used to machine learn electronic structure calculations. As a proof of principle, we show the success of an ANN potential energy surfaces for a dimer molecule with a Lennard-Jones potential.en
dc.description.sponsorshipUniversity of Ontario Institute of Technologyen
dc.identifier.urihttps://hdl.handle.net/10155/840
dc.language.isoenen
dc.subjectDensity functional theoryen
dc.subjectWater-metal interfacesen
dc.subjectMolecular dynamicsen
dc.subjectMachine learningen
dc.titleCharacterizing water-metal interfaces and machine learning potential energy surfacesen
dc.typeThesisen
thesis.degree.disciplineModelling and Computational Science
thesis.degree.grantorUniversity of Ontario Institute of Technology
thesis.degree.nameMaster of Science (MSc)

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