Journal
JOURNAL OF APPLIED PHYSICS
Volume 128, Issue 23, Pages -Publisher
AIP Publishing
DOI: 10.1063/5.0017507
Keywords
-
Categories
Funding
- National Science Foundation (NSF) by the Division of Chemistry (Macromolecular, Supramolecular, and Nanochemistry) [CHE-1507986]
- University of Virginia
- College of Arts and Sciences at University of Virginia
- National Science Foundation (NSF) [DMS-2015298]
Ask authors/readers for more resources
Growth of high quality two-dimensional transition metal dichalcogenide monolayers with the desired microstructure and morphology is critical for enabling key technological solutions. This is a non-trivial problem because the processing space is vast and lack of a priori guidelines impedes rapid progress. A machine learning approach is discussed that leverages the data present in published growth experiments to predict growth performance in regions of unexplored parameter space. Starting from the literature data on MoS 2 thin films grown using chemical vapor deposition (CVD), a database is manually constructed. Unsupervised and supervised machine learning methods are used to learn from the compiled data by extracting trends that underlie the formation of MoS 2 monolayers. Design rules are uncovered that establish the phase boundaries classifying monolayers from other possible outcomes, which offers future guidance of CVD experiments.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available