Determining the twist angle of stacked MoS2 layers using machine learning‐assisted low‐frequency interlayer Raman fingerprints

Abstract

AbstractThe investigation of twisted stacked few‐layer MoS2 has revealed novel electronic, optical, and vibrational properties over an extended period. For the successful integration of twisted stacked few‐layer MoS2 into a wide range of applications, it is crucial to employ a noninvasive, versatile technique for characterizing the layered architecture of these complex structures. In this work, we introduce a machine learning‐assisted low‐frequency Raman spectroscopy method to characterize the twist angle of few‐layer stacked MoS2 samples. A feedforward neural network (FNN) is utilized to analyze the low‐frequency breathing mode as a function of the twist angle. Moreover, using finite difference method (FDM) and density functional theory (DFT) calculations, we show that the low‐frequency Raman spectra of MoS2 are mainly influenced by the effect of the nearest and second nearest layers. A new improved linear chain model (TA‐LCM) with taking the twist angle into the consideration is developed to understand the interlayer breathing modes of stacked few‐layer MoS2. This approach can be extended to other 2D materials systems and provides an intelligent way to investigate naturally stacked and twisted interlayer interactions.