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Article

Machine Learning Analysis of Raman Spectra of MoS2

by 1,2, 1,2,*, 1,2, 1,2, 1,2, 1,2, 1,2 and 1,2,3,4,*
1
Laboratory of Micro-Nano Optoelectronic Materials and Devices, Key Laboratory of Materials for High-Power Laser, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
2
Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
3
State Key Laboratory of High Field Laser Physics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
4
CAS Center for Excellence in Ultra-Intense Laser Science (CEULS), Shanghai 201800, China
*
Authors to whom correspondence should be addressed.
Nanomaterials 2020, 10(11), 2223; https://doi.org/10.3390/nano10112223
Received: 28 September 2020 / Revised: 26 October 2020 / Accepted: 6 November 2020 / Published: 9 November 2020
(This article belongs to the Special Issue Characterization, Synthesis and Applications of 2D Nanomaterials)
Defects introduced during the growth process greatly affect the device performance of two-dimensional (2D) materials. Here we demonstrate the applicability of employing machine-learning-based analysis to distinguish the monolayer continuous film and defect areas of molybdenum disulfide (MoS2) using position-dependent information extracted from its Raman spectra. The random forest method can analyze multiple Raman features to identify samples, making up for the problem of not being able to effectively identify by using just one certain variable with high recognition accuracy. Even some dispersed nucleation site defects can be predicted, which would commonly be ignored under an optical microscope because of the lower optical contrast. The successful application for classification and analysis highlights the potential for implementing machine learning to tap the depth of classical methods in 2D materials research. View Full-Text
Keywords: 2D materials; machine learning; random forest algorithm; Raman spectrum 2D materials; machine learning; random forest algorithm; Raman spectrum
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MDPI and ACS Style

Mao, Y.; Dong, N.; Wang, L.; Chen, X.; Wang, H.; Wang, Z.; Kislyakov, I.M.; Wang, J. Machine Learning Analysis of Raman Spectra of MoS2. Nanomaterials 2020, 10, 2223. https://doi.org/10.3390/nano10112223

AMA Style

Mao Y, Dong N, Wang L, Chen X, Wang H, Wang Z, Kislyakov IM, Wang J. Machine Learning Analysis of Raman Spectra of MoS2. Nanomaterials. 2020; 10(11):2223. https://doi.org/10.3390/nano10112223

Chicago/Turabian Style

Mao, Yu, Ningning Dong, Lei Wang, Xin Chen, Hongqiang Wang, Zixin Wang, Ivan M. Kislyakov, and Jun Wang. 2020. "Machine Learning Analysis of Raman Spectra of MoS2" Nanomaterials 10, no. 11: 2223. https://doi.org/10.3390/nano10112223

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