In this project, we aim to develop a general framework to calculate system behaviors from microstructure images. To establish the generalizability of the approach, we employ convolutional neural networks (CNNs) to process microstructure images from two domains - polymer nanocomposites (PNCs) and granular biomaterials. We show that this approach can accurately predict system behaviors across microstructure types and therefore support inverse design, enabling new microstructures to be generated according to specified system behaviors.
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