Exploring topological spin order by inverse Hamiltonian design: A new stabilization mechanism for square skyrmion crystals

TL;DR

This paper introduces an inverse Hamiltonian design framework using machine learning to engineer real-space spin models, enabling the stabilization of square skyrmion crystals without traditional prerequisites.

Contribution

It presents a novel method for constructing spin models through inverse Hamiltonian design, broadening the understanding of topological spin order stabilization mechanisms.

Findings

Square skyrmion crystals can be stabilized without Dzyaloshinskii-Moriya interaction.

Long-range exchange interactions are key to stabilizing unconventional topological textures.

The framework is universally applicable to various magnetic systems.

Abstract

We propose a framework to construct a real-space spin model based on the inverse Hamiltonian design. The method provides an efficient way of realizing unconventional topological spin textures by optimizing the interaction parameters. In order to demonstrate its usefulness, we show that the tuning of the long-range exchange interactions can give rise to a square skyrmion crystal even without factors that have been previously identified as prerequisites for its stabilization, such as Dzyaloshinskii-Moriya interaction, multi-spin interaction, and bond-dependent magnetic anisotropy. Moreover, we elucidate the essence for the emergence of the square skyrmion crystal by classifying the parameter sets we get by the method. Since the present framework by adopting machine learning techniques can be universally applied to any magnetic system irrespective of lattice structures, it serves as the efficient construction of an effective spin model and the understanding of the stabilization mechanisms for unconventional topological spin orders.