Constrained Random Phase Approximation of the effective Coulomb interaction in lattice models of twisted bilayer graphene
Tuomas I. Vanhala, Lode Pollet

TL;DR
This paper employs the constrained random phase approximation to analyze the effective Coulomb interactions in lattice models of twisted bilayer graphene, considering screening effects from moire bands and providing detailed momentum-dependent dielectric properties.
Contribution
It introduces a numerically efficient method to compute the polarization function and effective interactions in twisted bilayer graphene, accounting for twist-angle dependence and screening effects.
Findings
Polarization exhibits three momentum regimes: quadratic at small, angle-independent at large, and twist-angle dependent at intermediate.
Dielectric function peaks near the magic angle, indicating strong screening at intermediate distances.
Effective Coulomb interaction decays slower than 1/r at intermediate distances, remaining largely unscreened at large distances.
Abstract
Recent experiments on twisted bilayer graphene show the urgent need for establishing a low-energy lattice model for the system. We use the constrained random phase approximation to study the interaction parameters of such models taking into account screening from the moire bands left outside the model space. Based on an atomic-scale tight-binding model, we develop a numerically tractable approximation to the polarization function and study its behavior for different twist angles. We find that the polarization has three different momentum regimes. For small momenta, the polarization is quadratic, leading to a linear dielectric function expected for a two-dimensional dielectric material. For large momenta, the polarization becomes independent of the twist angle and approaches that of uncoupled graphene layers. In the intermediate momentum regime, the dependence on the twist-angle is…
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.
