Numerical Linked-Cluster Algorithms. II. t-J models on the square lattice

TL;DR

This paper applies a numerical linked-cluster (NLC) algorithm to the t-J model on a square lattice, demonstrating its effectiveness in calculating thermodynamic properties at intermediate temperatures where other methods struggle.

Contribution

It introduces the application of NLC to the t-J model, showing advantages over high-temperature expansions and Lanczos methods in certain temperature regimes.

Findings

NLC converges without extrapolation in a significant temperature window.

NLC results agree well with HTE and FTLM after extrapolation.

NLC provides better control and accuracy at intermediate temperatures.

Abstract

We discuss the application of a recently introduced numerical linked-cluster (NLC) algorithm to strongly correlated itinerant models. In particular, we present a study of thermodynamic observables: chemical potential, entropy, specific heat, and uniform susceptibility for the t-J model on the square lattice, with J/t=0.5 and 0.3. Our NLC results are compared with those obtained from high-temperature expansions (HTE) and the finite-temperature Lanczos method (FTLM). We show that there is a sizeable window in temperature where NLC results converge without extrapolations whereas HTE diverges. Upon extrapolations, the overall agreement between NLC, HTE, and FTLM is excellent in some cases down to 0.25t. At intermediate temperatures NLC results are better controlled than other methods, making it easier to judge the convergence and numerical accuracy of the method.