Document Type
Poster
Publication Date
Summer 2024
Abstract
Molecular beam epitaxy (MBE) is an ultra–high vacuum crystal growth technique for the synthesis of high–purity materials. MBE allows for controlled deposition to the precision of a single atomic layer.
Understanding the optimal synthesis conditions for MBE can enhance experiment design and accelerate the discovery of new materials.
In this study, we apply materials informatics – investigating the utility of both quantum and conventional machine learning algorithms – to a decade’s worth of plasma–assisted MBE (PAMBE) data of GaN and InN. The goal is to investigate the relationships between PAMBE growth conditions and figures of merit for semiconductor crystals.
Department
Computer Science
WMU ScholarWorks Citation
Messecar, Andrew S.; Durbin, Steven M.; and Makin, Robert A., "Quantum & Classical Machine Learning Studies of Semiconductor Crystal Epitaxy" (2024). Waldo Library Student Exhibits. 13.
https://scholarworks.wmich.edu/student_exhibits/13