Machine Learning Determination of the Twist Angle of Bilayer Graphene by Raman Spectroscopy: Implications for van der Waals Heterostructures

Abstract

With the increasing interest in twisted bilayer graphene (tBLG) of the past years, fast, reliable, and nondestructive methods to precisely determine the twist angle are required. Raman spectroscopy potentially provides such a method, given the large amount of information about the state of the graphene that is encoded in its Raman spectrum. However, changes in the Raman spectra induced by the stacking order can be very subtle, thus making the angle identification tedious. In this work, we propose the use of machine learning (ML) analysis techniques for the automated classification of the Raman spectrum of tBLG into a selected range of twist angles. The ML classification proposed here is low computationally demanding, providing fast and accurate results with a ∼99% agreement with the manual labeling of the spectra. The flexibility and noninvasive nature of the Raman measurements, paired with the predictive accuracy of the ML, is expected to facilitate the exploration of the emerging research field of twisted van der Waals heterostructures. Moreover, the present work showcases how the currently available open-source tools facilitate the study and integration of ML-based techniques.

Twist Angle-Dependent Molecular Intercalation and Sheet Resistance in Bilayer Graphene

“…For the STEM observations, the BLG was transferred onto a transmission electron microscope (TEM) grid without using a PMMA protecting layer. To investigate the angle dependence with one specimen and to reduce the amount of contamination, such as PMMA, we used BLG grown on a Cu–Ni(111) thin film instead of stacking two graphene layers. , This is because CVD-grown BLG contains different twist angles and STEM can measure different areas while checking the twist angle of the host BLG. Intercalation was then performed for the BLG/TEM grid inside a Pyrex tube, and the tube was opened just before the STEM measurement.…”

“…However, most of the previous studies on intercalation in BLG have relied on mechanical exfoliation of graphite, which mainly contains AB-stacked BLG. By using uniform large-area BLG grown on Cu–Ni(111) alloy films by chemical vapor deposition (CVD), it is possible to obtain BLG with spatial variation of the stacking angle. − By intercalating MoCl 5 molecules in such BLG, we found that intercalation is more facile in the twisted BLG regions than in the AB-stacked BLG regions . However, it is still not clear how the stacking angle (twist angle) influences intercalation, because a large variety of stacking angles exist in CVD-grown BLG. , …”

“…25 However, it is still not clear how the stacking angle (twist angle) influences intercalation, because a large variety of stacking angles exist in CVD-grown BLG. 22,24 In this work, we investigated the influence of the stacking angle on metal chloride intercalation by fabricating artificially stacked BLG grains with controlled twist angles. Cointercalation of a AlCl 3 −CuCl 2 mixture with BLG was performed.…”

Excitation and detection of acoustic phonons in nanoscale systems

“…Another interesting property of these modes is that they are also sensitive to the stacking orientation, and the spectral properties of the modes (relative intensities, linewidth, and peak position) can vary as a function of the stacking angle. [69][70][71] These spectral changes are associated with a change in the interlayer coupling strength as well as a change in the crystal symmetry generated by the stacking. This can be observed in Fig.…”

Hydrogenated Graphene with Tunable Poisson’s Ratio Using Machine Learning: Implication for Wearable Devices and Strain Sensors

“…In recent years, numerous new materials and metamaterials have been successfully discovered. To determine their properties, machine learning (ML) is considered a promising tool. − The ML method was used to search for materials and structures with desirable properties such as composite materials with optimal thermal conductivity, toughness, and strength, conductor materials for batteries, , graphene kirigami with high stretchability, , inflatable soft membranes, porous graphene structure with optimal thermal conductivity, thermoelectricity, and thermal transport properties of nanostructures, and other applications. − Several studies have focused on searching for metamaterials with auxeticity using the ML method. , Based on the results of finite element analysis, Wilt et al used the ML method to optimize an auxetic porous metamaterial with a re-entrant honeycomb unit cell at the macro scale.…”