Computing Curie temperature of two-dimensional ferromagnets in the presence of exchange anisotropy

TL;DR

This paper compares methods for calculating the Curie temperature in 2D ferromagnets, proposes a simple accurate formula, and identifies promising high-temperature 2D magnetic materials.

Contribution

It introduces a new closed-form formula for estimating the Curie temperature in 2D ferromagnets that outperforms mean-field approaches.

Findings

Green's function overestimates Curie temperature at high anisotropy.

RNSW underestimates Curie temperature compared to MC and Green's function.

Several high Curie temperature 2D ferromagnets identified, including Fe2F2 and MoI2.

Abstract

We compare three first-principles methods of calculating the Curie temperature in two-dimensional (2D) ferromagnetic materials (FM), modeled using the Heisenberg model, and propose a simple formula for estimating the Curie temperature with high accuracy that works for all common 2D lattice types. First, we study the effect of exchange anisotropy on the Curie temperature calculated using the Monte-Carlo (MC), the Green's function method, and the renormalized spin-wave (RNSW). We find that the Green's function overestimates the Curie temperature in high-anisotropy regimes compared to MC, whereas RNSW underestimates the Curie temperature compared to the MC and the Green's function. Next, we propose a closed-form formula for calculating the Curie temperature of 2D FMs, which provides an estimate of the Curie temperature greatly improving over the mean-field expression for magnetic material screening. We apply the closed-form formula to predict the Curie temperature 2D magnets screened from the C2DB database and discover several high Curie temperature FMs with Fe2F2 and MoI2 emerging as the most promising 2D ferromagnets. Finally, comparing to experimental results for CrI3, CrCl3, and CrBr3, we conclude that for small effective anisotropies, the Green's function-based equations are preferable, while, for larger anisotropies MC-based results are more predictive.