This EArly-concept Grant for Exploratory Research (EAGER) grant supports fundamental research to develop scalable computational design tools to enable efficient and effective materials design. Computational material design (CMD), such as identifying optimal material microstructures to achieve desirable performance, receives a growing interest as sophisticated material designs can be subsequently realized using advanced processing techniques such as additive manufacturing. Conceptually, solving CMD problems involves iterative search for the best solutions in a problem space. Since the cost of solution searching is sensitive to the size of the space, the lack of cost-efficiency hampers the application of existing CMD approaches to complex material systems, where the goodness of the material design depends on numerous details of the microstructure on multiple length scales. The use of CMD tools will enable the discovery of critical microstructure patterns and the reduction of the dimensionality of the problem space. The research will lead to efficient microstructure design and validation for high performance structural materials with superior durability and structural health. Therefore, results from this research will benefit various U.S. industries, and its economy and society. The required seamless integration of material science, engineering design, manufacturing, and data science will help to broaden student participation and positively impact engineering education.