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001032241 1001_ $$0P:(DE-Juel1)188821$$aHu, Yang$$b0$$ufzj
001032241 245__ $$aA data-driven strategy for phase field nucleation modeling
001032241 260__ $$a[London]$$bMacmillan Publishers Limited, part of Springer Nature$$c2024
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001032241 520__ $$aWe propose a data-driven strategy for parameter selection in phase field nucleation models using machine learning and apply it to oxide nucleation in Fe-Cr alloys. A grand potential-based phase field model, incorporating Langevin noise, is employed to simulate oxide nucleation and benchmarked against the Johnson-Mehl-Avrami-Kolmogorov model. Three independent parameters in the phase field simulations (Langevin noise strength, numerical grid discretization and critical nucleation radius) are identified as essential for accurately modeling the nucleation behavior. These parameters serve as input features for machine learning classification and regression models. The classification model categorizes nucleation behavior into three nucleation density regimes, preventing invalid nucleation attempts in simulations, while the regression model estimates the appropriate Langevin noise strength, significantly reducing the need for time-consuming trial-and-error simulations. This data-driven approach improves the efficiency of parameter selection in phase field models and provides a generalizable method for simulating nucleation-driven microstructural evolution processes in various materials.
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001032241 7001_ $$0P:(DE-Juel1)173887$$aWang, Kai$$b1$$eCorresponding author
001032241 7001_ $$0P:(DE-Juel1)130979$$aSpatschek, Robert$$b2$$ufzj
001032241 773__ $$0PERI:(DE-600)2925488-7$$a10.1038/s41529-024-00529-8$$gVol. 8, no. 1, p. 109$$n1$$p109$$tnpj Materials degradation$$v8$$x2397-2106$$y2024
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