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Title: Ab Initio Based Computational Studies of Metal Oxide - Water Interfaces
Authors: Ding, Zhutian
Advisors: Selloni, Annabella
Contributors: Chemistry Department
Keywords: aqueous interfaces
density functional theory
electronic structure
machine learning
metal oxides
molecular dynamics
Subjects: Computational chemistry
Physical chemistry
Issue Date: 2023
Publisher: Princeton, NJ : Princeton University
Abstract: The aqueous interfaces of metal oxides play an important role in geochemical processes, catalysis, and electrochemical applications. Despite the intense research efforts over the past few decades, there are many open questions to be answered about metal oxide - water interfaces such as the nature of adsorbed water, the mechanism of electron transfer, the behavior of charged species, as well as the structure and dynamics of interfacial water at amorphous metal oxide - water interfaces. This dissertation aims to investigate some of the open questions via first principles based simulations for the aqueous interfaces of SiO2, MgO, and amorphous TiO2, all of which have important technical applications and are prototypical metal oxides. By using ab initio molecular dynamics (AIMD) based on a hybrid density functional, we investigate the detailed adsorption structure and dynamics of water at the aqueous interface of pristine and defected MgO. Also using AIMD based on a hybrid density functional, we study the behavior of a lithium impurity at the aqueous interface of SiO2 and its interaction with a localized electron donated by the lithium impurity in terms of electronic energy levels. Since the interplay between electronic energy levels and water dynamics governs interfacial charge transfer, we further study the behavior of an excess localized electron donated by an Al dopant at and out of equilibrium at the aqueous interface of Al-doped MgO. We identify two pathways of electron transfer from the conduction band of MgO to interfacial product states and characterize the pathways using Marcus theory. Recognizing the lack of understanding on the surfaces and aqueous interfaces of amorphous TiO2, we develop deep neural network potentials (DPs) trained on density functional theory and investigate the structure and dynamics of interfacial water at amorphous TiO2 interfaces using a combination of DP-based and ab initio molecular dynamics simulations. Overall, this dissertation has provided greater insights into the aqueous interfaces of the metal oxides studied, and has developed computational framework and procedure that can be broadly applicable to more general systems.
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Chemistry

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