Interfacial interactions between materials in complex heterostructures can dominate the material's response in manymodern-day energy-related devices and processes. Considerable research has been dedicated towards addressing the profound effects of interfaces. Here, first-principles-based quantum mechanical simulations are discussed to characterize the interfacial materials properties of two systems. First, density-functional theory (DFT) calculations were performed for ceramic oxide grain boundaries in undoped and doped CeO2. Second, the development, theoretical framework, and utilization of high-throughput, workflow-based, DFT calculations are presented to model the synthesis of two-dimensional (2D) heterostructured materials. Utilizing this workflow, predictive machine learning models were created to elucidate key interface-property relationships in 2D heterostructured materials. The DFT simulations reveal that the Σ3(111)/[101] grain boundary was energetically more stable than theΣ3(121)/[101]grain boundary due to the larger atomic coherency in the Σ3(111)/[101] grain boundary plane. The alkaline-earth metal-doped grain boundary energies demonstrate a parabolic dependence on the size of the solutes, interfacial strain, and packing density of the grain boundary. The grain boundary energies were stabilized upon Ca, Sr, and Ba doping whereas Be and Mg render them energetically unstable. The electronic density of states reveals that no defect states were present in/above the band gap. The thermodynamic trapping of oxygen vacancies in the near grain boundary region was not significantly impacted by the presence of Ca-solute ions. However, the migration energy barriers within the grain boundary core were dramatically reduced with high local Ca-solute concentrations, around 0.3 eV-0.5 eV. Chapter 5 and Chapter 6 discusses the development of the open-source, high-throughput computational "synthesis"based workflow package Hetero2d and the application of Hetero2d using 52 Janus 2D materials and 19 metallic, cubic phase, elemental substrates. The 438 Janus 2D-substrate pairs were analyzed by identifying substrate surfaces that stabilize metastable Janus 2D materials, characterizing their effects on the post-adsorbed 2D materials, and identifying the bonding between the 2D material and substrate. Machine learning models were applied to predict the binding energy, z-separation, and charge transfer of the Janus 2D-substrate pairs providing insight into the critical properties which factor into these properties.

Oxygen transfer reactions are central to many catalytic processes, including those underlying automotive exhaust emissions control and clean energy conversion. The catalysts used in these applications typically consist of metal nanoparticles dispersed on reducible oxides (e.g., Pt/CeO2), since reducible oxides can transfer their lattice oxygen to reactive adsorbates at the metal-support interface. There are many outstanding questions regarding the atomic and nanoscale spatial variation of the Pt/CeO2 interface, Pt metal particle, and adjacent CeO2 oxide surface during catalysis. To this end, a range of techniques centered around aberration-corrected environmental transmission electron microscopy (ETEM) were developed and employed to visualize and characterize the atomic-scale structural behavior of CeO2-supported Pt catalysts under reaction conditions (in situ) and/or during catalysis (operando). A model of the operando ETEM reactor was developed to simulate the gas and temperature profiles during conditions of catalysis. Most importantly, the model provides a tool for relating the reactant conversion measured with spectroscopy to the reaction rate of the catalyst that is imaged on the TEM grid. As a result, this work has produced a truly operando TEM methodology, since the structure observed during an experiment can be directly linked to quantitative chemical kinetics of the same catalyst. This operando ETEM approach was leveraged to investigate structure-activity relationships for CO oxidation over Pt/CeO2 catalysts. Correlating atomic-level imaging with catalytic turnover frequency reveals a direct relationship between activity and dynamic structural behavior that (a) destabilizes the supported Pt particle, (b) marks an enhanced rate of oxygen vacancy creation and annihilation, and (c) leads to increased strain and reduction in the surface of the CeO2 support. To further investigate the structural meta-stability (i.e., fluxionality) of 1 – 2 nm CeO2-supported Pt nanoparticles, time-resolved in situ AC-ETEM was employed to visualize the catalyst’s dynamical behavior with high spatiotemporal resolution. Observations are made under conditions relevant to the CO oxidation and water-gas shift (WGS) reactions. Finally, deep learning-based convolutional neural networks were leveraged to develop novel denoising techniques for ultra-low signal-to-noise images of catalytic nanoparticles.