Dresden 2026 – scientific programme

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HL: Fachverband Halbleiterphysik

HL 24: 2D Materials IV – Emerging materials and properties

HL 24.7: Talk

Wednesday, March 11, 2026, 11:15–11:30, POT/0081

Influence of Defects, Doping and Layer Twisting on Phonon Dispersions in Bilayer Graphene and MoS₂ from Machine-Learned Force Fields — •Sabuhi Badalov and Harald Oberhofer — Chair for Theoretical Physics VII and Bavarian Center for Battery Technologies, University of Bayreuth

In two-dimensional (2D) layered materials, phonon dispersion plays a central role in determining transport and electronic properties. In bilayer systems, phonon dispersion is highly sensitive to the stacking configuration, twist angle, defects, and doping types. Using state-of-the-art machine learning force fields trained on the first-principles data, we perform large-scale phonon calculations for bilayer graphene and MoS₂ to investigate how layer twist and defect density alter phonon spectra and interlayer vibrational modes. We also analyze the influence of p- and n-type doping on phonon dispersions and their interactions with the underlying electronic structure. Our results reveal characteristic shifts in low-energy acoustic and optical branches that are directly linked to microscopic structural motifs, including Moiré-induced phonon properties. These findings provide microscopic input for future calculations of electron-phonon coupling and possible phonon-mediated superconducting states in twisted and doped 2D materials and establish machine learning force fields as a powerful framework for exploring phonon-electron interaction in quantum materials.

Keywords: 2D materials; DFT; Phonons; Machine-learned force fields; Electron--phonon coupling