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@ARTICLE{Song:1032346,
      author       = {Song, Hengxu and Nguyen, Binh Duong and Govind, Kishan and
                      Berta, Dénes and Ispánovity, Péter Dusán and Legros,
                      Marc and Sandfeld, Stefan},
      title        = {{E}nabling quantitative analysis of in situ {TEM}
                      experiments: {A} high-throughput, deep learning-based
                      approach tailored to the dynamics of dislocations},
      journal      = {Acta materialia},
      volume       = {282},
      issn         = {1359-6454},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2024-06168},
      pages        = {120455 -},
      year         = {2025},
      abstract     = {In situ TEM is by far the most commonly used microscopy
                      method for imaging dislocations, i.e., line-like defects in
                      crystalline materials. However, quantitative image analysis
                      so far was not possible, implying that also statistical
                      analyses were strongly limited. In this work, we created a
                      deep learning-based digital twin of an in situ TEM straining
                      experiment, additionally allowing to perform matching
                      simulations. As application we extract spatio-temporal
                      information of moving dislocations from experiments carried
                      out on a Cantor high entropy alloy and investigate the
                      universality class of plastic strain avalanches. We can
                      directly observe “stick–slip motion” of single
                      dislocations and compute the corresponding avalanche
                      statistics. The distributions turn out to be scale-free, and
                      the exponent of the power law distribution exhibits
                      independence on the driving stress. The introduced
                      methodology is entirely generic and has the potential to
                      turn meso-scale TEM microscopy into a truly quantitative and
                      reproducible approach.},
      cin          = {IAS-9},
      ddc          = {670},
      cid          = {I:(DE-Juel1)IAS-9-20201008},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001349894500001},
      doi          = {10.1016/j.actamat.2024.120455},
      url          = {https://juser.fz-juelich.de/record/1032346},
}