A data-driven strategy for phase field nucleation modeling

npj Materials Degradation, vol. 8

Abstract

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.

Authors 3

1. Yang Hu Aachen Georesources and Materials Engineering

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Georesources and Materials Engineering, RWTH Aachen University, Aachen, 52062, Germany

   Institute of Energy Materials and Devices IMD-1, Forschungszentrum Jülich GmbH, Jülich, 52428, Germany

2. Kai Wang corresponding

   Forschungszentrum Jülich · Technische Universität Darmstadt

   Affiliation as printed

   Institute of Energy Materials and Devices IMD-1, Forschungszentrum Jülich GmbH, Jülich, 52428, Germany

   Mechanics of Functional Materials Division, Institute of Materials Science, Technical University of Darmstadt, Darmstadt, 64287, Germany

3. Robert Spatschek Aachen Georesources and Materials Engineering

   RWTH Aachen University · Forschungszentrum Jülich · Jülich Aachen Research Alliance

   Affiliation as printed

   Georesources and Materials Engineering, RWTH Aachen University, Aachen, 52062, Germany

   Institute of Energy Materials and Devices IMD-1, Forschungszentrum Jülich GmbH, Jülich, 52428, Germany

   JARA Energy, Jülich, 52428, Germany