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@ARTICLE{Vimal:910446,
      author       = {Vimal, M. and Sandfeld, S. and Prakash, A.},
      title        = {{G}rain segmentation in atomistic simulations using
                      orientation-based iterative self-organizing data analysis},
      journal      = {Materialia},
      volume       = {21},
      issn         = {2589-1529},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {FZJ-2022-03835},
      pages        = {101314 -},
      year         = {2022},
      abstract     = {Atomistic simulations have now established themselves as an
                      indispensable tool in understanding deformation mechanisms
                      of materials at the atomic scale. Large scale simulations
                      are regularly used to study the behavior of polycrystalline
                      materials at the nanoscale. In this work, we propose a
                      method for grain segmentation of an atomistic configuration
                      using an unsupervised machine learning algorithm that
                      clusters atoms into individual grains based on their
                      orientation. The proposed method, called the Orisodata
                      algorithm, is based on the iterative self-organizing data
                      analysis technique and is modified to work in the
                      orientation space. The working of the algorithm is
                      demonstrated on a 122 grain nanocrystalline thin film sample
                      in both undeformed and deformed states. The Orisodata
                      algorithm is also compared with two other grain segmentation
                      algorithms available in the open-source visualization tool
                      Ovito. The results show that the Orisodata algorithm is able
                      to correctly identify deformation twins as well as regions
                      separated by low angle grain boundaries. The model
                      parameters have intuitive physical meaning and relate to
                      similar thresholds used in experiments, which not only helps
                      obtain optimal values but also facilitates easy
                      interpretation and validation of results.},
      cin          = {IAS-9},
      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:000793064300002},
      doi          = {10.1016/j.mtla.2022.101314},
      url          = {https://juser.fz-juelich.de/record/910446},
}