On the Limits of Predictivity in Microstructural Evolution Simulations: Ensemble Simulations of Polycrystalline Grain Growth

Abstract Theoretical models for polycrystalline grain growth are deterministic. However, although computer simulations based on these models reproduce average features and trends, they do not reliably predict experimental growth trajectories for individual grains. Disagreement between experiment and simulation has generally been attributed to shortcomings in the computational instantiation; however, even after several decades of improving the physical bases of computational models, a perfect match has not been achieved. In this study, we examine the sources of uncertainty in simulation and experiment during polycrystalline grain growth. In ensembles of nominally identical molecular dynamics simulations, growth trajectories of individual grains can vary significantly due to discrete events (topological transformations) that have cascading effects on the microstructural ensemble. The type and timing of these microstructural events are extraordinarily sensitive to atomic-scale processes. When we compare ensemble simulation results to experimental outcomes, we find that the ensemble simulation delineates a range of possible outcomes, and the experimental results fall within that range. The implication is that polycrystalline grain growth has a fundamental, aleatoric uncertainty that limits our ability to predict its outcomes. Simulation and experiment can never agree perfectly; however, simulations can predict the range of possible experimental outcomes.