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  • Quantum and classical machine learning investigation of synthesis–structure relationships in epitaxially grown wide band gap semiconductors
Title: Quantum and classical machine learning investigation of synthesis–structure relationships in epitaxially grown wide band gap semiconductors
Several hundred plasma-assisted molecular beam epitaxy synthesis experiments of GaN and ZnO thin film crystals were organized into data sets that correlate the operating parameters selected for growth to two figures of merit: a binary determination of surface morphology, and a continuous Bragg–Williams measure of lattice ordering (S2). Quantum as well as conventional supervised machine learning algorithms were optimized and trained on the data, enabling a comparison of their generalization performance. The models displaying the best generalization performance on each data set were subsequently used to predict each figure of merit across the ZnO and GaN processing spaces.  more » « less
Award ID(s):
2003581
PAR ID:
10549056
Author(s) / Creator(s):
; ;
Publisher / Repository:
Springer Link
Date Published:
Journal Name:
MRS Communications
Volume:
14
Issue:
4
ISSN:
2159-6867
Page Range / eLocation ID:
660 to 666
Subject(s) / Keyword(s):
machine learning quantum machine learning crystal growth molecular beam epitaxy
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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