Revealing the correlated phonon properties in Raman spectra of graphene using machine learning

Raman spectroscopy is commonly used for characterization of graphene. The correlations between Raman peak attributes (e.g. peak width, position, asymmetry) indicate important information about physical properties of graphene, such as layer thickness, defects, doping levels, dielectric screening, and strain. Here we use machine learning techniques to find and study the relevant correlation of Raman spectral parameters, and reveal the properties of graphene in different dielectric environments. Graphene has shown different doping levels and screening effects on different substrates. We found that the 2D peak asymmetry reveals the charge doping level with a strong correlation. We analyze Raman spatial mapping data of graphene in different dielectric environments to find an efficient way to evaluate the phonon properties of graphene with Raman spectra data, which is an important reference for fabricating graphene-based devices. This method helps us understand correlations in graphene in different screening environments.