Tight-binding and density-functional study of the Raman tensor in two-dimensional massive Dirac fermion systems
Sel\c{c}uk Parlak, Abhishek Kumar, Runhan Li, Maia G. Vergniory, Ion Garate

TL;DR
This study confirms that the unique Raman response features predicted for massive Dirac fermions in 2D materials are robust, using tight-binding and density-functional calculations to validate earlier continuum model predictions.
Contribution
The paper demonstrates the robustness of predicted Raman tensor features in 2D massive Dirac systems through realistic tight-binding and DFT calculations, extending previous continuum model results.
Findings
Quantized phase difference in Raman tensor elements depends on Dirac mass sign
Selection rule for Raman intensity under circular polarization is confirmed
Results are consistent across continuum, tight-binding, and DFT models
Abstract
Recently, two unusual features were theoretically predicted for the Raman response of out-of-plane phonons in magnetic two-dimensional materials hosting massive Dirac fermions. First, the phase difference between certain Raman tensor elements was found to be quantized to , sensitive only to the sign of the Dirac fermion mass. Second, a selection rule was identified in the Raman intensity under circularly polarized light, which generalizes the well-known optical valley selection rule. These predictions were based on a low-energy effective model in the continuum approximation. Here, we test the robustness of those results for more realistic theoretical approaches. First, we calculate the Raman tensor for an electronic tight-binding model on a honeycomb lattice with broken time-reversal and inversion symmetries. Second, we compute the Raman tensor from density-functional theory…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
