Resolution of the Two-Dimensional Ferromagnetic Spin-3/2 Ising Model via Cluster Growth

TL;DR

This paper introduces a hierarchical cluster growth method to efficiently solve the spin-3/2 Ising model, accurately reproducing experimental magnetic properties of a 2D ferromagnet with reduced computational complexity.

Contribution

The paper presents a novel hierarchical cluster approach that overcomes exponential complexity in solving large 2D spin-3/2 Ising models, enabling accurate modeling of experimental systems.

Findings

Successfully reproduces magnetization and specific heat curves of CrI₃ monolayer

Demonstrates finite residual entropy at low temperatures

Provides a computationally efficient framework for complex magnetic systems

Abstract

We propose a computational methodology based on a hierarchical cluster growth process to solve spin-3/2 Ising models efficiently. The method circumvents the exponential complexity (\(4^{N}\)) of the canonical ensemble partition function by iteratively constructing finite magnetic clusters of size \(N_g\), where the effective spin state of a site in generation \(g+1\) is determined by the local magnetization of a cluster from generation \(g\). This approach, which shares conceptual ground with effective field theories, allows the study of systems of effectively very large size \(N = N_0 (N_g)^{g}\). We apply the formalism to the ferromagnetic spin-3/2 Ising model on a honeycomb lattice, modeling the monolayer CrI$_3$, a prototypical two-dimensional Ising magnet. The model, calibrated using the experimental transition temperature (\(T_{c} \simeq 45\) K), successfully reproduces key experimental features: the temperature dependence of the magnetization \(m(T)\), including its inflection point, and the broadened peak in the specific heat \(c_v(T)\). We also compute the entropy \(s(T)\), finding a finite residual value at low temperatures consistent with the system's double degeneracy. Our results demonstrate that this hierarchical cluster method provides a quantitatively accurate and computationally efficient framework for studying complex magnetic systems.