is shows that the model performs well for grain sizes ranging from ∼ 5 nm to ∼ 40 nm. The trained RF model can be used for fast and accurate prediction of grain sizes for a given set of Phase-field simulation input parameters. To explain its good performance, we analyzed it using the SHapley Additive exPlanations (SHAP) approach . SHAP employs the classic Shapley values from game theory to quantify the contributions of individual features to the output of a ML model. In our case, it quantifies the importance of the 5 Phase-field simulation input parameters in our grain size prediction model. Figure summarizes the SHAP results. The beeswarm plot at the top shows the SHAP values of all data points for the 5 features (simulation input parameters) and is colored by the feature values. Diffusivity is found to be the most important feature followed by volume fraction (V f ), aspect ratio ( ), particle diameter (P d ), and surface-to-grain-boundary energy ratio (γ r ) is found to be the least important feature. Larger values of diffusivity, aspect ratio, and particle diameter lead to larger predicted grain sizes, whereas larger values of particle volume fraction and surface-to-grain-boundary energy ratio lead to smaller predicted grain sizes. The bar plot at the bottom of Figure shows that the pinning effect, which is the combined contribution of volume fraction, aspect ratio, and diameter of the pinning particle, is about equal or slightly more important than diffusivity in determining the predicted final grain size.