Unraveling the Correlation between Raman and Photoluminescence in Monolayer MoS 2 through Machine‐Learning Models
[1];
[1]
- Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02139 USA
- Department of Physics Massachusetts Institute of Technology Cambridge MA 02139 USA
- Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
- Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02139 USA, Physical Science and Engineering Division King Abdullah University of Science &, Technology (KAUST) Thuwal 23955‐6900 Saudi Arabia
- Physical Science and Engineering Division King Abdullah University of Science &, Technology (KAUST) Thuwal 23955‐6900 Saudi Arabia, Department of Chemical System Engineering University of Tokyo Tokyo 113–8654 Japan
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 MoS 2 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.
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- SC0021940
- OSTI ID:
- 1877718
- Journal Information:
- Advanced Materials, Journal Name: Advanced Materials Journal Issue: 34 Vol. 34; ISSN 0935-9648
- Publisher:
- Wiley Blackwell (John Wiley & Sons)Copyright Statement
- Country of Publication:
- Germany
- Language:
- English
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