Deep‐Learning‐Enabled Fast Raman Identification of the Twist Angle of Bi‐Layer Graphene

Abstract Twisted bilayer graphene (TBG) has drawn considerable attention due to its angle‐dependent electrical, optical, and mechanical properties, yet preparing and identifying samples at specific angles on a large scale remains challenging and labor‐intensive. Here, a data‐driven strategy that leverages Raman spectroscopy is proposed in combination with deep learning to rapidly and non‐destructively decode and predict the twist angle of TBG across the full angular range. By processing high‐dimensional Raman data, the deep learning model extracts hidden information to achieve precise twist angle identification. This approach is further extended to a 2D plane, enabling accurate orientational mapping within individual samples. Through interpretability analysis, the model is validated in conjunction with first‐principles theoretical calculations, ensuring robust and explainable results. This data‐driven methodology not only facilitates efficient TBG characterization but also introduces a broadly applicable framework for studying other angle‐dependent 2D materials, thereby advancing the field of material spectroscopy and analysis.