CNN-Based Classifier for Automated Identification of Magnetic States in Spin Dynamics Simulations

TL;DR

This paper introduces a deep learning classifier using EfficientNetV1B0 to automatically identify nine magnetic states from spin configuration images generated by atomistic simulations.

Contribution

It presents a novel application of CNNs for classifying complex magnetic states directly from simulation visualizations, improving automation and accuracy.

Findings

Successfully classified nine magnetic states including AFM skyrmions and stripe domains.

Used atomistic spin dynamics simulations to generate and visualize spin configurations.

Demonstrated effectiveness of CNN-based approach for magnetic state identification.

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.