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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},
}