Going beyond k.p theory: a general method for obtaining effective Hamiltonians in both high and low symmetry situations
N. Ray (1), F. Rost (1), D. Weckbecker (1), M. Vogl (1), S. Sharma, (2), R. Gupta (2), O. Pankratov (1), and S. Shallcross (1) ((1) Lehrstuhl, f\"ur Theoretische Festk\"orperphysik, Erlangen, Germany, (2), Max-Planck-Institut f\"ur Mikrostrukturphysik, Halle, Germany)

TL;DR
This paper introduces a versatile method for deriving effective Hamiltonians that surpasses traditional k.p theory, applicable to systems with varying symmetry, and demonstrates its effectiveness on diverse low-dimensional materials including graphene variants.
Contribution
The authors develop an exact mapping from two-centre tight-binding to continuum Hamiltonians, enabling analysis of high and low symmetry systems with systematic correction capabilities.
Findings
Derived effective Hamiltonians for various 2D materials.
Identified charge pooling and localized current states in dislocation networks.
Provided a unified framework for describing stacking deformations in bilayer graphene.
Abstract
We provide a method for the generation of effective continuum Hamiltonians that goes beyond the well known k.p method in being equally effective in both high, and low (or no) symmetry situations. Our approach is based on a surprising exact map of the two-centre tight-binding method onto a compact continuum Hamiltonian, with a precise condition given for the hermiticity of the latter object. We apply this method to a broad range of low dimensional systems of both high and low symmetry: graphene, graphdiyne, {\gamma}-graphyne, 6,6,12-graphyne, twist bilayer graphene, and partial dislocation networks in Bernal stacked bilayer graphene. For the single layer systems the method yields Hamiltonians for the ideal lattices, as well as a systematic theory for corrections due to deformation. In the case of bilayer graphene we provide a compact expression for an effective field capable of…
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.
