Phys. Rev. B 109, 155139 (2024)
We report on the investigation of periodic superstructures in twisted bilayer graphene (tBLG) van der Waals heterostructures, where one of the graphene layers is aligned to hexagonal boron nitride (hBN). Our theoretical simulations reveal that if the ratio of the resulting two moiré unit-cell areas is a simple fraction, the graphene/hBN moiré lattice acts as a staggered potential, breaking the degeneracy between tBLG AA sites. This leads to additional band gaps at energies where a subset of tBLG AA sites is fully occupied. These gaps manifest as Landau fans in magnetotransport, which we experimentally observe in an aligned tBLG/hBN heterostructure. Our study demonstrates the identification of commensurate tBLG/hBN van der Waals heterostructures by magnetotransport, highlights the persistence of moiré effects on length scales of tens of nanometers, and represents an interesting step forward in the ongoing effort to realize designed quantum materials with tailored properties.

We are pleased to announce that our group member, Jan-Lucas Uslu, has been elected as Chairperson of the Board for AI in Physics, i.e. the DPG Working Group on Physics, Modern IT and Artificial Intelligence (AKPIK). This position is a testament to Jan's outstanding dedication to the field of AI in physics. We congratulate him for this achievement!

Mach. Learn.: Sci. Technol. 5, 015027 (2024)
The most widely used method for obtaining high-quality two-dimensional (2D) materials is through mechanical exfoliation of bulk crystals. Manual identification of suitable flakes from the resulting random distribution of crystal thicknesses and sizes on a substrate is a time-consuming, tedious task. Here, we present a platform for fully automated scanning, detection, and classification of 2D materials, the source code of which we make openly available. Our platform is designed to be accurate, reliable, fast, and versatile in integrating new materials, making it suitable for everyday laboratory work. The implementation allows fully automated scanning and analysis of wafers with an average inference time of 100 ms for images of 2.3 Mpixels. The developed detection algorithm is based on a combination of the flakes’ optical contrast toward the substrate and their geometric shape. We demonstrate that it is able to detect the majority of exfoliated flakes of various materials, with an average recall (AR50) between 67% and 89%. We also show that the algorithm can be trained with as few as five flakes of a given material, which we demonstrate for the examples of few-layer graphene, WSe₂, MoSe₂, CrI₃, 1T-TaS₂ and hexagonal BN. Our platform has been tested over a two-year period, during which more than 10⁶ images of multiple different materials were acquired by over 30 individual researchers

Nano Letters 24, 1867 (2024)
Few-layer graphene possesses low-energy carriers that behave as massive Fermions, exhibiting intriguing properties in both transport and light scattering experiments. Lowering the excitation energy of resonance Raman spectroscopy down to 1.17 eV, we target these massive quasiparticles in the split bands close to the K point. The low excitation energy weakens some of the Raman processes that are resonant in the visible, and induces a clearer frequency-separation of the substructures of the resonance 2D peak in bi- and trilayer samples. We follow the excitation-energy dependence of the intensity of each substructure, and comparing experimental measurements on bilayer graphene with ab initio theoretical calculations, we trace back such modifications on the joint effects of probing the electronic dispersion close to the band splitting and enhancement of electron–phonon matrix elements.

On 16.01.2024, Michael Schmitz successfully defended his PhD thesis. Congratulations to Dr. Michael Schmitz on this commendable accomplishment!