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Review: 2D material property characterizations by machine-learning-assisted microscopies.

Authors :
Si, Zhizhong
Zhou, Daming
Yang, Jianlei
Lin, Xiaoyang
Source :
Applied Physics A: Materials Science & Processing. Apr2023, Vol. 129 Issue 4, p1-13. 13p. 1 Color Photograph, 1 Diagram, 1 Chart, 4 Graphs.
Publication Year :
2023

Abstract

Microscopy characterization techniques can provide intuitive images of 2D materials with certain spatial resolutions. At the same time, machine-learning algorithms, which have experienced tremendous advancement in image processing over passed decades, are able to extract comprehensive information directly from a large scale of the images. Combining microscopy characterization techniques with machine-learning algorithms can offer insight into the structures and properties of 2D materials with the advantages of high automation, high accuracy, and high throughput. Herein, we will give a review of this interdisciplinary area, from foundations and progress to challenges and potential opportunities. The developments in this field are first overviewed according to its characterization techniques. Then, this review focuses on the theoretical and practical foundations of machine-learning-assisted microscopies for 2D material property characterizations, followed by two case studies to illustrate the implementation details. Finally, challenges and opportunities are addressed for future research and industrialized applications. We hope this review article can provide a clear guideline for both the academic society and general readers and inspire researchers for further explorations of this promising area. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09478396
Volume :
129
Issue :
4
Database :
Academic Search Index
Journal :
Applied Physics A: Materials Science & Processing
Publication Type :
Academic Journal
Accession number :
163149912
Full Text :
https://doi.org/10.1007/s00339-023-06543-y