Air Force Office of Scientific Research (AFOSR), under the Department of Defense (DOD), under award number FA9550-12-1-0458; and National Institute of Standards and Technology (NIST), under Award No. 70NANB14H012.

Project Team Members:

Northwestern University

University of Michigan-Ann Arbor

Georgia Institute of Technology

Northwestern University - EECS Dept.

Representation Design for Heterogeneous Microstructures


The goal of this project is to solve a key research question in designing microstructural materials systems: how to represent the microstructural design space quantitatively using a descriptor set that is sufficient yet small enough to be tractable.

In short, if all you have as the raw microstructure representation are the scanning electron microscopy (SEM) samples, as below --

Figure 1. SEM samples of polymer nanocomposites, regarded as the raw form of microstructure representation.

-- how to obtain a quantitative sense of how "good" this sample is, and subsequently, how to make it "better" in terms of certain property of interest.


We develop a 4-step machine learning based method to exploit the microstructure-property database (Figure 2 below). The four steps include:

  1. Elimination of redundant descriptors using descriptor-descriptor correlation analysis;
  2. Microstructure correlation function-based supervised learning for further dimension reduction;
  3. Property-based supervised learning to identify key descriptors;
  4. Determination of microstructure design variables based on the optimization criteria of maximizing the impact score and minimizing the within-group correlations of the selected descriptor set.
Figure 2. 4-step framework of machine learning based microstructure descriptor identification.

Steps 1 and 2 are image analysis-based procedures, which don't require expensive Finite Element Analysis (FEA) simulations. These two steps will provide a fast reduction of the size of a candidate descriptor set. Both steps 1 and 2 involve supervised learning. Step 3 needs structure-property data from either high-fidelity simulations or from literature. Step 4 is an optimization-based descriptor subset selection process.



Related Links


This work is supported by AFOSR (Air Force Office of Scientific Research), Department of Defense (DOD) under Award No. FA9550-12-1-0458; and by National Institute of Standards and Technology (NIST), under Award No. 70NANB14H012.

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