A data-driven strategy for phase field nucleation modeling
Abstract We propose a data-driven strategy for parameter selection in phase field nucleation models using machine Resistance Cable 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