This thesis reports on incorporating various computational methods and tools, including first-principles calculations, mesoscale modeling, stochastic modeling, and Artificial intelli-gence to study structure-property relations in wide bandgap semiconductors. In the first project, we present first-principles calculations that elucidate the growth mecha-nism of ZnO photocatalytic materials for water-splitting applications used in sustainable energy storage. Polar surfaces of ZnO are known to have higher photoelectrochemical ac-tivity increasing water splitting efficiency. However, they are known to be unstable and less probable to form under normal conditions. In this project, we demonstrate these high-energy surfaces could be stabilized under certain growth conditions. This approach sug-gests a general solution for controlling the growth morphology, and it can be applied to other compounds to tailor their structure and obtain materials that are not normally stable. Further, we present a comprehensive study of diamond-based systems for application in high-power and high-frequency electronic devices. This includes two major projects to find solutions for SD of diamond. the first project studies the possibility of using hexagonal Bo-ron Nitride (hBN) and graphene 2D materials as acceptor or interface layer in diamond-based heterostructures. The second project reports modeling and analysis of amorphous vanadium pentoxide slabs as candidates for SD of diamond. Chapter six of this thesis introduces a novel AI-based approach to accelerate computational studies of molecule-surface (and molecule-molecule) interactions. To this end, we present an interactive method that couples Gaussian Processes, Bayesian Inference, and molecular dynamics simulations to accelerate the search for the minimum energy structure in mole-cule-surface interactions. This method addresses the problem of dealing with multiple con-figurations with similar energies. It enables making accurate predictions from relatively small datasets and quantifying the uncertainty associated with each prediction. The last chapter of this thesis introduces a Monte Carlo-based raytracing model, named LightCapture, that simulates light absorption in a microfluidic water-treatment reactor. The LightCapture model was developed to predict geometry - light interaction correlations in reactors consisting of micropillars. This is a critical step in determining the reactor’s overall photocatalytic efficiency. To evaluate the performance, the model was applied to determine light capture efficiency in microreactors that use an array of TiO2 photocatalytic micropil-lars, which are being developed for treating recycled water on spacecraft during deep space missions, and the results were in great agreement with experimental tests. This thesis provides guiding principles for accelerated discovery of wide bandgap semi-conductors. Many of the presented methods and tools have been successfully utilized to provide guiding principles for experimental fabrication.

Heat transfer is ubiquitous in both naturally occurring and engineered materials. As technology progresses, the length- and time-scales of thermal transport decreases, becoming comparable with the mean free paths and relaxation times of the vibrations that drive it. Increasingly, an atomistic-level understanding of thermal transport is pivotal in predicting and controlling heat transport in materials and devices. Modeling approaches that permit an atomistic understanding of heat transport, and the implementation of complex approximations of the phonon Boltzmann transport formalism include classical molecular dynamics and density functional theory (DFT). Results are presented for equilibrium molecular dynamics (EMD) simulations of the thermal conductivity of a series of clustering and non-clustering point defects in graphite using the Green–Kubo method, aimed to advance our knowledge of the evolution of the microstructure of graphite while in service in a graphite-moderated nuclear reactor. The Green–Kubo method — commonly used for predicting transport properties by scientists and engineers across fields — relates the property of interest to the lifetime of fluctuations in its thermodynamic driving potential. The integral of the autocorrelation fluctuations requires a long averaging time to reduce remnant noise and is a principal source of error. A new approach is proposed to quantify — on-the-fly — the uncertainty on transport properties computed using the Green-Kubo formulation, based on recognizing that the integrated noise is a random walk. EMD is also used to explore thermal transport in breathing metal--organic frameworks (MOFs), coveted for numerous applications due to their large surface area and modularity. A simple geometric model of thermal conductivity is proposed as a heuristic for the quick evaluation of transport in flexible MOFs, and a quantum-based approach is undertaken to explore deviations from the heuristic, such as rattler modes and phonon-focusing. Phonon properties calculated with DFT for (1) uranium dioxide and (2) silicon, for fuel and spintronics applications respectively, are also briefly discussed.