Journal
ADVANCED MATERIALS
Volume 34, Issue 34, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202202911
Keywords
2D materials; machine learning; molybdenum disulfide; photoluminescence; Raman spectroscopy
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Funding
- MathWorks
- U.S. Army Research Office through the Institute for Soldier Nanotechnologies at MIT [W911NF-18-2-0048]
- U.S. Army Research Office (ARO) MURI project [W911NF-18-1-0431]
- STC Center for Integrated Quantum Materials, NSF [DMR-1231319]
- King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [OSR-2018-CARF/CCF-3079]
- KAUST Research Translational Fund (RTF)
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This study systematically explores the correlation between photoluminescence and Raman spectra in 2D transition metal dichalcogenides, and disentangles the contributions of strain and doping using machine learning models. It provides a methodology for applying machine learning to characterizations of 2D materials.
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.
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