Data Assimilation for Microstructure Evolution in Kinetic Monte Carlo

Modeling grain growthGrain growth has been a subject of interest in computational material science, as it occurs in thermal-based processing methods such as annealing and sintering. Kinetic Monte CarloKinetic Monte Carlo with Potts model is often used as an integrated computational materials engineering (ICME) grain growthGrain growth model and can generate high-fidelity synthetic microstructures. In this paper, we offer a data-driven stochastic calculus perspective on the kinetics of grain growthGrain growth and model the microstructure evolution through the lens of stochastic differential equations, based on Langevin dynamics and Fokker-PlanckFokker-Planck equation to forecast the grain size distribution. We demonstrate that our proposed approach agrees reasonably well with the hybrid Potts-phase field model using SPPARKSSPPARKS in forecasting the long-term evolution of grain size distribution.