To quickly predict grain size, we use a machine learning regression model to model the outcomes of our high-throughput Phase-field simulations, as the impact of various input parameters on the final grain size Figure : This plot shows the grain size evolution over time for the simulated microstructures depicted in Figure (a)-(f), where D is instantaneous average grain size and D0 is the initial average grain size. The data points represent the average grain size in the microstructure at each time step. As shown in the plot, all microstructures exhibit an initial stage of rapid grain growth, followed by a slower growth stage, until a limiting grain size is reached at the end of the simulation time (t = 15000). It is interesting to note that the microstructures with higher volume fractions and higher surface-to-grain boundary energy ratio exhibit slower grain growth and reach a smaller limiting grain size, indicating a stronger Zener pinning effect. Conversely, microstructures with larger particle sizes, lower particle volume fractions, and lower surface-to-grain boundary energy ratios exhibit faster grain growth and reach a larger limiting grain size, indicating weaker Zener pinning. The effect of particle aspect ratio and mobility on grain growth is also evident in the plot, with smaller aspect ratios and higher mobilities leading to faster grain growth and larger limiting grain size. These results are consistent with the observations made from the microstructure analysis and further, confirm the importance of the investigated parameters in controlling grain growth and Zener pinning in the presence of second-phase precipitates.