Unraveling the Correlation between Raman and Photoluminescence in Monolayer MoS2 through Machine Learning Models
Dr. Ang-Yu Lu
PhD Student
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
*The organization and the title are those when awarded
Research summary
Two-dimensional (2D) transition metal dichalcogenides (TMDCs)＊1 with intense and tunable photoluminescence (PL) ＊2 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, due to its highly nonlinear nature, a comprehensive understanding of the correlation between PL and Raman spectra in monolayer MoS2 remains elusive.
In this work, Dr. Lu has systematically explored the connections between PL signatures and Raman＊ modes, providing comprehensive insights into the physical mechanisms correlating PL and Raman features. This analysis further disentangles the strain and doping contributions from the Raman spectra through machine-learning models. First, he deployed a DenseNet＊to predict PL maps using spatial Raman maps＊. Moreover, he applyed a gradient-boosted trees model (XGBoost)＊ with Shapley additive explanation (SHAP)＊ to evaluate the impact of individual Raman features on PL features, allowing him to link the strain and doping of monolayer MoS2. Lastly, Dr. Lu adopted a support vector machine (SVM)＊ to project PL features onto Raman frequencies. This 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 PL.
Compounds consisting of transition metal elements and chalcogen elements such as sulfur, selenium, and tellurium, which have a two-dimensional layered structure.
A phenomenon in which electrons in a material absorb light and emit light specific to that material.
A phenomenon in which the crystal lattice of a material scatters light specific to that material. The scattered light can be spectrally analyzed to determine the characteristics of the material.
A convolutional neural network used to learn features of images and patterns. Convolution refers to the process of detecting local patterns within an image.
A mapping of Raman spectra obtained from different positions on the surface of a material using Raman spectroscopy.
A machine learning algorithm known as gradient boosting.
A method for evaluating the contribution of features to the predictions of a machine learning model.
A machine learning algorithm used for tasks such as classification and regression.
Compounds consisting of transition metal elements and chalcogen elements such as sulfur, selenium, and tellurium, which have a two-dimensional layered structure.
A phenomenon in which electrons in a material absorb light and emit light specific to that material.
A phenomenon in which the crystal lattice of a material scatters light specific to that material. The scattered light can be spectrally analyzed to determine the characteristics of the material.
A convolutional neural network used to learn features of images and patterns. Convolution refers to the process of detecting local patterns within an image.
A mapping of Raman spectra obtained from different positions on the surface of a material using Raman spectroscopy.
A machine learning algorithm known as gradient boosting.
A method for evaluating the contribution of features to the predictions of a machine learning model.
A machine learning algorithm used for tasks such as classification and regression.
