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@INPROCEEDINGS{Aldarawsheh:1044424,
author = {Aldarawsheh, Amal and Alia, Ahmed and Blügel, Stefan},
title = {{CNN}-based {C}lassification of {M}agnetic {S}tates from
{A}tomistic {S}imulations in thin films},
reportid = {FZJ-2025-03185},
year = {2025},
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 a fully
automated deep learning model that employs an
EfficientNetV1B0 Convolutional Neural Network (CNN) 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 $\textit{Spirit}$ code, then visualized with
VFRendering script. Our model achieves a classification
accuracy and F1-score of $99\\%,$ significantly
outperforming established CNN baselines and demonstrating
exceptional capability in distinguishing closely related
magnetic states.},
month = {Jun},
date = {2025-06-02},
organization = {Virtual Materials Design 2025 -
Karlsruhe, Karlsuhe (Germany), 2 Jun
2025 - 5 Jun 2025},
subtyp = {After Call},
cin = {PGI-1},
cid = {I:(DE-Juel1)PGI-1-20110106},
pnm = {5211 - Topological Matter (POF4-521)},
pid = {G:(DE-HGF)POF4-5211},
typ = {PUB:(DE-HGF)6},
url = {https://juser.fz-juelich.de/record/1044424},
}