Unraveling the Correlation between Raman and Photoluminescence in Monolayer MoS2 through Machine Learning Models

Two-dimensional (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 crystallinities 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, we systematically explore the connections between PL signatures and Raman modes, providing comprehensive insights into the physical mechanisms correlating PL and Raman features. Our analysis further disentangles the strain and doping contributions from the Raman spectra through machine learning models. First, we deploy a DenseNet to predict PL maps by spatial Raman maps. Moreover, we apply a gradient boosted trees model (XGBoost) with Shapley additive explanation (SHAP) to bridge the impact of individual Raman features in PL features, allowing us to link the strain and doping of monolayer MoS2. Last, we adopt a support vector machine (SVM) to project PL features on Raman frequencies. Our work may serve as a methodology for applying machine learning in 2D material characterizations and providing the knowledge for tuning and synthesizing 2D semiconductors for high-yield photoluminescence.