Unraveling the Correlation between Raman and Photoluminescence in Monolayer MoS <sub>2</sub> through Machine‐Learning Models
Name
Advanced Materials - 2022 - Lu - Unraveling the Correlation between Raman and Photoluminescence in Monolayer MoS2 through.pdf
Description
Published version
Size
1.7 MB
Format
Adobe PDF
Checksum (MD5)
18aacf702cd76d45a72db805089b0cb3
Author(s)
Lu, Ang‐Yu
Martins, Luiz Gustavo Pimenta
Shen, Pin‐Chun
Chen, Zhantao
Park, Ji‐Hoon
Xue, Mantian
Han, Jinchi
Mao, Nannan
Chiu, Ming‐Hui
Palacios, Tomás
Date Issued
July 5, 2022
Journal
Advanced Materials
Publisher
Wiley
Citation
Lu, Ang‐Yu, Martins, Luiz Gustavo Pimenta, Shen, Pin‐Chun, Chen, Zhantao, Park, Ji‐Hoon et al. 2022. "Unraveling the Correlation between Raman and Photoluminescence in Monolayer MoS 2 through Machine‐Learning Models." Advanced Materials.
Version
Final published version
Abstract
2D transition metal dichalcogenides (TMDCs) with intense and tunable photoluminescence (PL) have opened up new opportunities for optoelectronic and photonic applications such as light-emitting diodes, photodetectors, and single-photon emitters. Among the standard characterization tools for 2D materials, Raman spectroscopy stands out as a fast and non-destructive technique capable of probing material's crystallinity and perturbations such as doping and strain. However, a comprehensive understanding of the correlation between photoluminescence and Raman spectra in monolayer MoS2 remains elusive due to its highly nonlinear nature. Here, the connections between PL signatures and Raman modes are systematically explored, providing comprehensive insights into the physical mechanisms correlating PL and Raman features. This study's analysis further disentangles the strain and doping contributions from the Raman spectra through machine-learning models. First, a dense convolutional network (DenseNet) to predict PL maps by spatial Raman maps is deployed. Moreover, a gradient boosted trees model (XGBoost) with Shapley additive explanation (SHAP) to bridge the impact of individual Raman features in PL features is applied. Last, a support vector machine (SVM) to project PL features on Raman frequencies is adopted. This work may serve as a methodology for applying machine learning to characterizations of 2D materials.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1002/adma.202202911
