Profile approach for recognition of three-dimensional magnetic structures

TL;DR

This paper introduces a profile-based method for visualizing and classifying complex three-dimensional magnetic structures, enabling accurate phase identification even near transition points using simple neural networks.

Contribution

The paper presents a novel profile approach that simplifies 3D magnetic configurations into sortable vectors, improving phase classification accuracy in complex magnetic states.

Findings

Effective separation of different magnetic phases.

High accuracy in phase classification near critical points.

Ability to distinguish states within the same phase.

Abstract

We propose an approach for low-dimensional visualisation and classification of complex topological magnetic structures formed in magnetic materials. Within the approach one converts a three-dimensional magnetic configuration to a vector containing the only components of the spins that are parallel to the z axis. The next crucial step is to sort the vector elements in ascending or descending order. Having visualized profiles of the sorted spin vectors one can distinguish configurations belonging to different phases even with the same total magnetization. For instance, spin spiral and paramagnetic states with zero total magnetic moment can be easily identified. Being combined with a simplest neural network our profile approach provides a very accurate phase classification for three-dimensional magnets characterized by complex multispiral states even in the critical areas close to phases transitions. By the example of the skyrmionic configurations we show that profile approach can be used to separate the states belonging to the same phase.