Application of polynomial-expansion Monte Carlo method to a spin-ice Kondo lattice model

TL;DR

This paper demonstrates an efficient polynomial-expansion Monte Carlo method for large-scale simulations of a spin-ice Kondo lattice model, showing convergence and improved performance over traditional methods.

Contribution

The study introduces a polynomial-expansion Monte Carlo algorithm that enhances simulation efficiency for complex spin-electron systems on pyrochlore lattices.

Findings

The polynomial-expansion method converges with increasing polynomials and truncation distance.

It outperforms conventional exact diagonalization algorithms in large-scale simulations.

The approach enables detailed analysis of spin-ice Kondo lattice models.

Abstract

We present the results of Monte Carlo simulation for a Kondo lattice model in which itinerant electrons interact with Ising spins with spin-ice type easy-axis anisotropy on a pyrochlore lattice. We demonstrate the efficiency of the truncated polynomial expansion algorithm, which enables a large scale simulation, in comparison with a conventional algorithm using the exact diagonalization. Computing the sublattice magnetization, we show the convergence of the data with increasing the number of polynomials and truncation distance.