Most technologically useful materials are polycrystalline microstructures composed of a myriad of small monocrystalline grains delimited by grain boundaries. An understanding of the evolution of grain boundaries and associated grain growth (coarsening) is essential in determining the properties of materials across multiple scales. Despite tremendous progress in formulating microstructural models, however, current descriptions do not fully account for various grain growth mechanisms, detailed grain topologies and the effects of different time scales on microstructural evolution. As a result, conventional theories have limited predictive capability. The goal of the project is to develop a predictive theory of grain growth in polycrystalline materials through the construction of novel, closely integrated data-driven numerical simulation and mathematical modeling combined with data analytics, analysis, and a set of critical experiments. 

This interdisciplinary project, requiring the complementary expertise of applied mathematicians and materials scientists, is firmly aligned with the Materials Genome Initiative. The new knowledge and tools that will emerge from the project will have a profound impact on the performance and reliability of polycrystalline materials used in many technologically useful systems and structures, thereby expediting advanced materials development and deployment. Predictive computational algorithms and data will be made available and accessible to other researchers. For the training of the next-generation materials workforce, in addition to mentoring of graduate and undergraduate students, the PIs will participate in outreach activities and will continue to work towards increasing diversity and broadening participation within STEM.