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@ARTICLE{Hu:1032241,
      author       = {Hu, Yang and Wang, Kai and Spatschek, Robert},
      title        = {{A} data-driven strategy for phase field nucleation
                      modeling},
      journal      = {npj Materials degradation},
      volume       = {8},
      number       = {1},
      issn         = {2397-2106},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {FZJ-2024-06088},
      pages        = {109},
      year         = {2024},
      abstract     = {We propose a data-driven strategy for parameter selection
                      in phase field nucleation models using machine learning and
                      apply it to oxide nucleation in Fe-Cr alloys. A grand
                      potential-based phase field model, incorporating Langevin
                      noise, is employed to simulate oxide nucleation and
                      benchmarked against the Johnson-Mehl-Avrami-Kolmogorov
                      model. Three independent parameters in the phase field
                      simulations (Langevin noise strength, numerical grid
                      discretization and critical nucleation radius) are
                      identified as essential for accurately modeling the
                      nucleation behavior. These parameters serve as input
                      features for machine learning classification and regression
                      models. The classification model categorizes nucleation
                      behavior into three nucleation density regimes, preventing
                      invalid nucleation attempts in simulations, while the
                      regression model estimates the appropriate Langevin noise
                      strength, significantly reducing the need for time-consuming
                      trial-and-error simulations. This data-driven approach
                      improves the efficiency of parameter selection in phase
                      field models and provides a generalizable method for
                      simulating nucleation-driven microstructural evolution
                      processes in various materials.},
      cin          = {IMD-1},
      ddc          = {530},
      cid          = {I:(DE-Juel1)IMD-1-20101013},
      pnm          = {1221 - Fundamentals and Materials (POF4-122)},
      pid          = {G:(DE-HGF)POF4-1221},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001345098600001},
      doi          = {10.1038/s41529-024-00529-8},
      url          = {https://juser.fz-juelich.de/record/1032241},
}