  The vibrational properties of twisted bilayer graphene (tBLG) show complex
features, due to the intricate energy landscape of its low-symmetry
configurations. A machine learning-based approach is developed to provide a
continuous model between the twist angle and the simulated Raman spectra of
tBLGs. Extracting the structural information of the twist angle from Raman
spectra corresponds to solving a complicated inverse problem. Once trained, the
machine learning regressors (MLRs) quickly provide predictions without human
bias and with an average of 98% of the data variance being explained by the
model. The significant spectral features learned by MLRs are analyzed revealing
the intensity profile near the calculated G-band to be the most important
feature. The trained models are tested on noise-containing test data
demonstrating their robustness. The transferability of the present models to
experimental Raman spectra is discussed in the context of validation of the
level of theory used for the construction of the analyzed database. This work
serves as a proof of concept that machine-learning analysis is a potentially
powerful tool for interpretation of Raman spectra of tBLG and other 2D
materials.
