Skip to main navigation Skip to search Skip to main content

A high-order regularized delta-Chebyshev method for computing spectral densities

  • Jinjing Yi
  • , Daniel Massatt
  • , Andrew Horning
  • , Mitchell Luskin
  • , J. H. Pixley
  • , Jason Kaye

Research output: Contribution to journal › Article › peer-review

Abstract

We introduce a numerical method for computing spectral densities, and apply it to the evaluation of the local density of states (LDOS) of Hamiltonians derived from tight-binding models. The approach, which we call the high-order delta-Chebyshev method, can be viewed as a variant of the popular regularized Chebyshev kernel polynomial method (KPM), but it uses a high-order accurate approximation of the δ -function to achieve rapid convergence to the thermodynamic limit for smooth spectral densities. The costly computational steps are identical to those for KPM, with high-order accuracy achieved by an inexpensive post-processing procedure. We apply the algorithm to tight-binding models of graphene and twisted bilayer graphene, demonstrating high-order convergence to the LDOS at non-singular points.

Original languageEnglish (US)
Article number115299
JournalJournal of Computational Physics
Volume567
DOIs
StatePublished - Dec 15 2026

All Science Journal Classification (ASJC) codes

  • Numerical Analysis
  • Modeling and Simulation
  • Physics and Astronomy (miscellaneous)
  • General Physics and Astronomy
  • Computer Science Applications
  • Computational Mathematics
  • Applied Mathematics

Keywords

  • Graphene
  • Hamiltonian
  • Kernel polynomial method
  • Spectral density

Fingerprint

Dive into the research topics of 'A high-order regularized delta-Chebyshev method for computing spectral densities'. Together they form a unique fingerprint.

Cite this