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MA: Fachverband Magnetismus

MA 55: Skyrmions III

MA 55.10: Vortrag

Freitag, 13. März 2026, 12:00–12:15, HSZ/0004

CNN-Based Classifier for Automated Identification of Magnetic States in Spin Dynamics Simulations — •Amal Aldarawsheh¹, Ahmed alia², and Stefan Blügel¹ — ¹Peter Grünberg Institute Forschungszentrum Jülich and JARA, D-52425 Jülich, Germany — ²Institute for Advanced Simulation (IAS-7), Forschungszentrum Jülich, 52425 Jülich, Germany

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 FM and, for the first time, 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. Experimental results show that the proposed model achieves an accuracy and F1-score of 99%, significantly outperforming established deep learning baselines.

Keywords: Artificial intelligence; Convolutional neural networks; Automatic classification; Deep learning; Magnetic spin textures