Journal Article

FZJ-2026-04171

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png CNN-based classifier for automated identification of magnetic states in spin dynamics simulations

Aldarawsheh, A. (Corresponding author)FZJ* ; Alia, A. (Corresponding author)FZJ* ; Blügel, S.FZJ*

2026
Springer Nature [London]

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Please use a persistent id in citations: doi:10.1038/s41598-026-54755-y  doi:10.34734/FZJ-2026-04171

Abstract: The identification and classification of different magnetic states are essential for understanding the complex behavior of magnetic systems. Traditional approaches that rely on handcrafted features or manual inspection often fall short, particularly when dealing with subtle or topologically complex spin textures. In this study, we present an automated deep learning model that employs an EfficientNetV1B0 Convolutional Neural Network to classify nine distinct magnetic states, including both ferromagnetic (FM) and antiferromagnetic (AFM) spin textures such as AFM skyrmions and AFM stripe domains. The spin configurations are generated through atomistic spin dynamics simulations using the Spirit code, then visualized with VFRendering to produce RGB images, which serve as inputs to the classification model. To train and evaluate the model, we created a new dataset of manually labeled RGB images. Evaluation results show that the proposed model achieves an accuracy and F1-score of 99%, outperforming the other deep learning baselines evaluated in this study.

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Contributing Institute(s):

1. Quanten-Theorie der Materialien (PGI-1)

Research Program(s):

1. 5211 - Topological Matter (POF4-521) (POF4-521)
2. 3D MAGiC - Three-dimensional magnetization textures: Discovery and control on the nanoscale (856538) (856538)
3. SFB 1238 C01 - Strukturinversionsasymmetrische Materie und Spin-Orbit-Phänomene mittels ab initio (C01) (319898210) (319898210)

Appears in the scientific report 2026

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