000			02109nam a2200253Ia 4500
003			MX-MdCICY
005			20250625162452.0
040			_cCICY
090			_aB-20619
245	1	0	_a2D material property characterizations by machine-learning-assisted microscopies
490	0		_vApplied Physics A, 129(4), p.248, 2023
520	3		_aMicroscopy 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.
650	1	4	_a2D MATERIALS
650	1	4	_aMICROSCOPY
650	1	4	_aPROPERTY CHARACTERIZATION
650	1	4	_aMACHINE LEARNING
700	1	2	_aSi, Z.
700	1	2	_aZhou, D.
700	1	2	_aYang, J.
700	1	2	_aLin, X.
856	4	0	_uhttps://drive.google.com/file/d/1vn-WAaFNuSv3exFOJIKAD_sGLPky4kOg/view?usp=drivesdk _zPara ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx
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