The ability to digitally design materials with microstructures optimized to achieve desired properties, is one of the long term goals of the materials field. Simulation-based materials design has the potential to dramatically reduce the need for expensive down-stream characterization and testing. However, this requires reliable algorithms and methodologies that incorporate variability and uncertainty in the design process, and are validated against physics-based models and experiments. Achieving the “digital design” goal requires the creation of a number of new methodologies that rely on the expertise of several research communities outside the materials field. The team we have assembled for this MURI program has broad expertise in experimental microstructure characterization, mathematical theory of microstructure, and design and informatics, and represents a microcosm of a computationally-integrated multi-university research and development laboratory.
This MURI program is carried out with Carnegie Mellon University as the lead organization, with six external universities are partners: Purdue University, Northwestern University, Caltech, Georgia Tech, University of Michigan, and University of Minnesota. The principal aim of our program is to create advanced methodologies for quantitative microstructure-property analysis and length scale bridging design, and efficient measurement of structure/time evolution, all implemented using optimized modeling and data mining techniques on HPC and multi-core platforms. The program will deliver algorithms for 3D reconstructions, optimization of microstructures, data storage and retrieval, among others; new mathematical models for microstructure-property relations in materials, a new thin-manifold description of material microstructures; and methodologies/frameworks for microstructure sensitive design as well as experimental validation of process design.