Kavli Affiliate: Chiara Daraio
| First 5 Authors: Zhi Chen, Alexander Ogren, Chiara Daraio, L. Catherine Brinson, Cynthia Rudin
| Summary:
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.
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