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Published March 28, 2022 | Submitted
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How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning


Metamaterials are composite materials with engineered geometrical micro- and meso-structures that can lead to uncommon physical properties, like negative Poisson's ratio or ultra-low shear resistance. Periodic metamaterials are composed of repeating unit-cells, and geometrical patterns within these unit-cells influence the propagation of elastic or acoustic waves and control dispersion. In this work, we develop a new interpretable, multi-resolution machine learning framework for finding patterns in the unit-cells of materials that reveal their dynamic properties. Specifically, we propose two new interpretable representations of metamaterials, called shape-frequency features and unit-cell templates. Machine learning models built using these feature classes can accurately predict dynamic material properties. These feature representations (particularly the unit-cell templates) have a useful property: they can operate on designs of higher resolutions. By learning key coarse scale patterns that can be reliably transferred to finer resolution design space via the shape-frequency features or unit-cell templates, we can almost freely design the fine resolution features of the unit-cell without changing coarse scale physics. Through this multi-resolution approach, we are able to design materials that possess target frequency ranges in which waves are allowed or disallowed to propagate (frequency bandgaps). Our approach yields major benefits: (1) unlike typical machine learning approaches to materials science, our models are interpretable, (2) our approaches leverage multi-resolution properties, and (3) our approach provides design flexibility.

Additional Information

The authors are grateful to M. Bastawrous, A. Lin, K. Liu, C. Zhong, O. Bilal, W. Chen, C. Tomasi, and S. Mukherjee for the feedback and assistance they provided during the development and preparation of this research. The authors acknowledge funding from the National Science Foundation under grant OAC-1835782, Department of Energy under grant DE-SC0021358, and National Research Traineeship Program under NSF grant DGE-2022040. The authors declare no competing interests.

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August 20, 2023
October 23, 2023