Computational Studies of Structure-Property Relations in Wide Bandgap Semiconductors

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