This project focuses on developing a major new capability for control of materials processing: the ability to monitor, evaluate and control the local materials structure of a system as it evolves during thermal processing in order to obtain optimal properties. This is enabled through new methodologies for integrating experiment, simulation, and data management. The specific project implementation comprises control of grain growth during annealing of polycrystalline metals. Monte Carlo and phase field simulations are used to predict the trajectory of microstructure evolution, and real-time measurements of local microstructure, crystallography and temperature using scanning electron microscopy methods are used to track these trajectories experimentally. When measurement shows significant deviation from the planned trajectory, a new trajectory is simulated and implemented through control algorithms. The experimental implementation comprises a multi-zone resistive heating array that enables controlled temperature distributions to be programmed across a macroscopic polycrystalline metal sample in-situ to a scanning electron microscope. The methodologies developed here should be extendible to acceleration of a wide range of process development cycles where specific internal materials structures are known to correlate to optimal materials properties and performance.