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@ARTICLE{Zhang:910448,
      author       = {Zhang, Chen and Song, Hengxu and Oliveros, Daniela and
                      Fraczkiewicz, Anna and Legros, Marc and Sandfeld, Stefan},
      title        = {{D}ata-mining of in-situ {TEM} experiments: {O}n the
                      dynamics of dislocations in {C}o{C}r{F}e{M}n{N}i alloys},
      journal      = {Acta materialia},
      volume       = {241},
      issn         = {1359-6454},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2022-03837},
      pages        = {118394 -},
      year         = {2022},
      abstract     = {High entropy alloys are a class of materials with many
                      significant improvements in terms of mechanical properties
                      as compared to “classical” alloys. The corresponding
                      structure-property relations are not yet entirely clear, but
                      it is commonly believed that the good mechanical performance
                      is strongly related to dislocation interactions with the
                      complex energy landscape formed due to alloying. Although
                      in-situ Transmission Electron Microscopy (TEM) allows
                      high-resolution studies of the structure and dynamics of
                      moving dislocations and makes the local obstacle/energy
                      “landscape” directly visible in the geometry of
                      dislocations; such observation, however, are merely
                      qualitative, and detailed three-dimensional analyses of the
                      interaction between dislocations and the energy landscape is
                      still missing. In this work, we utilized dislocations as
                      “probes” for the local energy maxima which play the role
                      of pinning points for the dislocation movement. To this end,
                      we developed a unique data-mining approach that can perform
                      coarse-grained spatio-temporal analysis, making ensemble
                      averaging of a considerable number of snapshots possible. We
                      investigate the effect of pinning points on the dislocation
                      gliding behavior of CoCrFeMnNi alloy during in-situ TEM
                      straining and find that (i) the pinning point strength
                      changes when dislocations glide through and (ii) the pinning
                      point moves along the direction close to the Burgers vector
                      direction. Our data-mining method can be applied to
                      dislocation motion in general, making it a useful tool for
                      dislocation research.},
      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) / MuDiLingo - A
                      Multiscale Dislocation Language for Data-Driven Materials
                      Science (759419)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)759419},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000878787500003},
      doi          = {10.1016/j.actamat.2022.118394},
      url          = {https://juser.fz-juelich.de/record/910448},
}