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@ARTICLE{Prakash:943387,
      author       = {Prakash, Aruna and Sandfeld, Stefan},
      title        = {{A}utomated {A}nalysis of {C}ontinuum {F}ields from
                      {A}tomistic {S}imulations {U}sing {S}tatistical {M}achine
                      {L}earning},
      journal      = {Advanced engineering materials},
      volume       = {24},
      number       = {12},
      issn         = {1438-1656},
      address      = {Frankfurt, M.},
      publisher    = {Deutsche Gesellschaft für Materialkunde},
      reportid     = {FZJ-2023-00981},
      pages        = {2200574 -},
      year         = {2022},
      abstract     = {Atomistic simulations of the molecular dynamics/statics
                      kind are regularly used to study small-scale plasticity.
                      Contemporary simulations are performed with tens to hundreds
                      of millions of atoms, with snapshots of these configurations
                      written out at regular intervals for further analysis.
                      Continuum scale constitutive models for material behavior
                      can benefit from information on the atomic scale, in
                      particular in terms of the deformation mechanisms, the
                      accommodation of the total strain, and partitioning of
                      stress and strain fields in individual grains. Herein, a
                      methodology is developed using statistical data mining and
                      machine learning algorithms to automate the analysis of
                      continuum field variables in atomistic simulations. Three
                      important field variables are focused on: total strain,
                      elastic strain, and microrotation. The results show that the
                      elastic strain in individual grains exhibits a unimodal
                      lognormal distribution, while the total strain and
                      microrotation fields evidence a multimodal distribution. The
                      peaks in the distribution of total strain are identified
                      with a Gaussian mixture model and methods to circumvent
                      overfitting problems are presented. Subsequently, the
                      identified peaks are evaluated in terms of deformation
                      mechanisms in a grain, which, e.g., helps to quantify the
                      strain for which individual deformation mechanisms are
                      responsible. The overall statistics of the distributions
                      over all grains are an important input for higher scale
                      models, which ultimately also helps to be able to
                      quantitatively discuss the implications for information
                      transfer to phenomenological models.},
      cin          = {IAS-9},
      ddc          = {660},
      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:000854299800001},
      doi          = {10.1002/adem.202200574},
      url          = {https://juser.fz-juelich.de/record/943387},
}