Non-classical methods for the simulation and design of quantum materials.

Materials science has transformed human technology, ranging from robust structural materials like steel alloys to the sophisticated semiconductor devices that drive modern electronics. Many of these materials can be studied and understood computationally through classical methods such as Density Functional Theory, which apply traditional first-principles simulations of material physics. However, the simulation of quantum materials (whose properties are fundamentally shaped by quantum mechanics) poses a significant challenge due to their intricate electronic structure and unique emergent phenomena, such as magnetism, topological states, and superconductivity. Classical methods often struggle to accurately model these systems, especially when quantum effects such as strong electron correlations or entanglement are involved. This has manifested as a significant research gap between the computational modeling of these materials and the data obtained through experimental measurements. This dissertation investigates non-classical approaches, specifically machine learning (ML) and quantum computing, to address these limitations. ML enables efficient simulation and prediction by uncovering hidden patterns in quantum systems, while quantum computing leverages the principles of quantum mechanics to simulate materials at a fundamentally precise level. Specifically, we introduce novel machine learning models for high-throughput screening of superconducting materials and the prediction of electronic band structure in solid state materials. We also present ongoing work applying this model to large and complex solid state systems and devices. We also introduce a new quantum computational method for efficiently simulating open quantum systems and leverage this method to simulate selected systems on modern quantum hardware. By developing novel ML architectures and quantum algorithms, this work aims to push the boundaries of quantum material simulation, bridging the gap between theory and experiment and accelerating the discovery of novel and potentially transformative materials.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Doctoral

Grantor

Baylor University.

Year dc:date.issued

2026

Author dc:creator

• Burdine, Colin M., 1999-

Advisor dc:contributor.advisor

• Blair, Enrique Pacis.

• Quantum computing.
• Quantum mechanics.
• Machine learning.
• Computational chemistry.
• Materials science.
• Solid state physics.

Statement dc:rights

• Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission.

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/2104/14765

OAI identifier oai:identifier

oai:baylor-ir.tdl.org:2104/14765