Dynamic annihilation pathways of magnetic skyrmions

TL;DR

This paper compares pseudospectral and micromagnetic models for simulating magnetic skyrmion annihilation, highlighting the importance of model choice and discretisation in accurately predicting skyrmion dynamics at various length scales.

Contribution

It introduces a pseudospectral approach that offers consistent skyrmion modeling across scales, capturing complex behaviors missed by conventional methods.

Findings

Model choice significantly affects annihilation field predictions.

Pseudospectral model captures skyrmion breathing and complex dynamics.

Discretisation impacts the accuracy of skyrmion stability simulations.

Abstract

The investigation of magnetic solitons often relies on numerical modeling to determine key features such as stability, annihilation, nucleation, and motion. However, as soliton sizes approach atomic length scales, the accuracy of these predictions become increasingly sensitive to the details of the numerical model. Here, we study the annihilation of two-dimensional magnetic skyrmions using a pseudospectral approach and compare its performance to that of conventional micromagnetic simulations. A central distinction between the models lies in their treatment of the exchange interaction, which governs the magnon dispersion relation and plays a crucial role to balance the uniaxial anisotropy to stabilise skyrmions. We demonstrate that both the choice of model and spatial discretisation significantly influence the skyrmion dynamics and the magnetic field required for annihilation. The pseudospectral model provides a consistent description across length scales and captures complex behaviours such as skyrmion breathing on its path to annihilation. Our results have direct implications in the state-of-the-art modeling of skyrmions and other two-dimensional textures and will impact the modeling of three-dimensional textures such as hopfions. More broadly, our approach will contribute to the development seamless multiscale model and optimization machine learning approaches for material discovery.