001044424 001__ 1044424
001044424 005__ 20250814202246.0
001044424 037__ $$aFZJ-2025-03185
001044424 1001_ $$0P:(DE-Juel1)185991$$aAldarawsheh, Amal$$b0$$eCorresponding author$$ufzj
001044424 1112_ $$aVirtual Materials Design 2025 - Karlsruhe$$cKarlsuhe$$d2025-06-02 - 2025-06-05$$wGermany
001044424 245__ $$aCNN-based Classification of Magnetic States from Atomistic Simulations in thin films
001044424 260__ $$c2025
001044424 3367_ $$033$$2EndNote$$aConference Paper
001044424 3367_ $$2DataCite$$aOther
001044424 3367_ $$2BibTeX$$aINPROCEEDINGS
001044424 3367_ $$2DRIVER$$aconferenceObject
001044424 3367_ $$2ORCID$$aLECTURE_SPEECH
001044424 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1755155257_1789$$xAfter Call
001044424 520__ $$aThe identification and classification of different magnetic states are essential for understanding the complex behavior of magnetic systems. Traditional approaches that rely on handcrafted features or manual inspection often fall short, particularly when dealing with subtle or topologically complex spin textures. In this study, we present a fully automated deep learning model that employs an EfficientNetV1B0 Convolutional Neural Network (CNN) to classify nine distinct magnetic states, including both FM and, for the first time, AFM spin textures such as AFM skyrmions and AFM stripe domains. The spin configurations are generated through atomistic spin dynamics simulations using the \textit{Spirit} code, then visualized with VFRendering script. Our model achieves a classification accuracy and F1-score of 99\%, significantly outperforming established CNN baselines and demonstrating exceptional capability in distinguishing closely related magnetic states.
001044424 536__ $$0G:(DE-HGF)POF4-5211$$a5211 - Topological Matter (POF4-521)$$cPOF4-521$$fPOF IV$$x0
001044424 7001_ $$0P:(DE-Juel1)185971$$aAlia, Ahmed$$b1$$ufzj
001044424 7001_ $$0P:(DE-Juel1)130548$$aBlügel, Stefan$$b2$$ufzj
001044424 909CO $$ooai:juser.fz-juelich.de:1044424$$pVDB
001044424 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)185991$$aForschungszentrum Jülich$$b0$$kFZJ
001044424 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)185971$$aForschungszentrum Jülich$$b1$$kFZJ
001044424 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)130548$$aForschungszentrum Jülich$$b2$$kFZJ
001044424 9131_ $$0G:(DE-HGF)POF4-521$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5211$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vQuantum Materials$$x0
001044424 9141_ $$y2025
001044424 9201_ $$0I:(DE-Juel1)PGI-1-20110106$$kPGI-1$$lQuanten-Theorie der Materialien$$x0
001044424 980__ $$aconf
001044424 980__ $$aVDB
001044424 980__ $$aI:(DE-Juel1)PGI-1-20110106
001044424 980__ $$aUNRESTRICTED