Publication Date

Summer 6-27-2022

Abstract

The piezoelectric response has been a measure of interest in density functional theory (DFT) for micro-electromechanical systems (MEMS) since the inception of MEMS technology. Piezoelectric-based MEMS devices find wide applications in automobiles, mobile phones, healthcare devices, and silicon chips for computers, to name a few. Piezoelectric properties of doped aluminum nitride (AlN) have been under investigation in materials science for piezoelectric thin films because of its wide range of device applicability. In this research using rigorous DFT calculations, high throughput ab-initio simulations for 23 AlN alloys are generated.

This research is the first to report strong enhancements of piezoelectric properties in group IVB metals -- Titanium (Ti), Zirconium (Zr), and Hafnium (Hf) and partial enhancements in group VB metals -- Niobium (Nb) and Tantalum (Ta). Additionally, using a deep learning predictive model, predictions are made for optimal atomic compositions of solutes in AlN for sputter deposition. To demonstrate the use of machine learning (ML) algorithms in bioinformatics, the importance of features using an extreme gradient boosting algorithm is investigated for the derivation of the association between disease and genes. Moreover, a framework for better ML interpretability is also proposed.

Degree Name

Statistics

Level of Degree

Doctoral

Department Name

Mathematics & Statistics

First Committee Member (Chair)

Nathan Jackson

Second Committee Member

James Degnan

Third Committee Member

Jeremy Yang

Fourth Committee Member

Ronald Christensen

Language

English

Keywords

statistics, computational, machine learning, bioinformatics, materials science

Document Type

Dissertation

Download

Share

COinS