We received a NSF EAGER award on Design of Engineering Material Systems
Max Yi Ren, Yang Jiao and Yongming Liu won a NSF EAGER award from the Design of Engineering Material Systems program.
Title: Efficient Reconstruction and Optimal Design of Multi-scale Material Systems through Deep Networks
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. This award supports fundamental research to develop scalable computational design tools to enable tractable CMD, by discovering the critical microstructure patterns and thus reducing 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.
Two core technical enablers for tractable CMD will be developed. These include (1) a generative statistical model that learns important local microstructure patterns at multiple length scales, and provides a two-way detail-preserving conversion between microstructures and their low-dimensional design representations; and (2) a cost-effective modeling framework for processing-structure-property mapping using physics-based simulations and active learning, to facilitate effective processing- and microstructure-level solution space exploration and optimal design. Key challenges will be addressed to enable scalable microstructure feature learning by taking into account the physical interpretability of the learned features. Novel deep network architectures and learning techniques will be investigated to this end. The statistical modeling of the processing-structure mapping will be achieved by a physics-based simulation tool where both phase morphology and crystallographic information are explicitly considered; the modeling of the structure-property mapping is achieved by a novel lattice-particle simulation method.