# Dynamical and thermal magnetic properties of the Kitaev spin liquid candidate $\alpha$-RuCl$_3$

## TL;DR

This study evaluates various effective Hamiltonians for $ ext{RuCl}_3$, revealing none fully explain experimental magnetic properties, and proposes a modified model aligning with both specific heat and neutron scattering data.

## Contribution

The paper systematically compares existing models and introduces a modified ab initio Hamiltonian that better captures experimental magnetic behaviors of $ ext{RuCl}_3$.

## Key findings

- No single current model explains all experimental data.
- First-principles models match high-temperature specific heat peaks but overestimate magnon energies.
- Modified model aligns with both specific heat and inelastic neutron scattering data.

## Abstract

What is the correct low-energy spin Hamiltonian description of $\alpha$-RuCl$_3$? The material is a promising Kitaev spin liquid candidate, but is also known to order magnetically, the description of which necessitates additional interaction terms. The nature of these interactions, their magnitudes and even signs, remain an open question. In this work we systematically investigate dynamical and thermodynamic magnetic properties of proposed effective Hamiltonians. We calculate zero-temperature inelastic neutron scattering (INS) intensities using exact diagonalization, and magnetic specific heat using a thermal pure quantum states method. We find that no single current model satisfactorily explains all observed phenomena of $\alpha$-RuCl$_3$. In particular, we find that Hamiltonians derived from first principles can capture the experimentally observed high-temperature peak in the magnetic specific heat, while overestimating the magnon energy at the zone center. In contrast, other models reproduce important features of the INS data, but do not adequately describe the magnetic specific heat. To address this discrepancy we propose a modified ab initio model that is consistent with both magnetic specific heat and low-energy features of INS data.