Spin dynamics in the Kitaev model with disorder: Quantum Monte Carlo study of dynamical spin structure factor, magnetic susceptibility, and NMR relaxation rate
Joji Nasu, Yukitoshi Motome

TL;DR
This study uses quantum Monte Carlo simulations to analyze how bond randomness and site dilution affect spin dynamics, magnetic susceptibility, and NMR relaxation in the Kitaev quantum spin liquid, revealing disorder-dependent modifications of low-energy excitations.
Contribution
It provides the first detailed analysis of disorder effects on dynamical properties of the Kitaev model using unbiased quantum Monte Carlo methods.
Findings
Disorder affects low-energy flux excitations differently: bond randomness softens peaks, site dilution smears peaks and introduces sharp features.
Zero-energy Majorana modes around vacancies survive up to high temperatures.
Weak and strong disorder lead to different behaviors in susceptibility and NMR relaxation, with immediate divergence in site dilution cases.
Abstract
We investigate the impact of two types of disorder, bond randomness and site dilution, on the spin dynamics in the Kitaev model on a honeycomb lattice. The ground state of this model is a canonical quantum spin liquid with spin fractionalization into two types of quasiparticles, itinerant Majorana fermions and localized fluxes. Using unbiased quantum Monte Carlo simulations, we calculate the temperature evolution of the dynamical spin structure factor, the magnetic susceptibility, and the NMR relaxation rate. In the dynamical spin structure factor, we find that the two types of disorder affect seriously the low-energy peak dominantly originating from the flux excitations, rather than the high-energy continuum from the Majorana excitations, in a different way: The bond randomness softens the peak to the lower energy with broadening, whereas the site dilution smears the peak and in…
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.
