Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning

Kavli Affiliate: Chiara Daraio

| First 5 Authors: Mary V. Bastawrous, Zhi Chen, Alexander C. Ogren, Chiara Daraio, Cynthia Rudin

| Summary:

Manipulating the dispersive characteristics of vibrational waves is
beneficial for many applications, e.g., high-precision instruments. architected
hierarchical phononic materials have sparked promise tunability of
elastodynamic waves and vibrations over multiple frequency ranges. In this
article, hierarchical unit-cells are obtained, where features at each length
scale result in a band gap within a targeted frequency range. Our novel
approach, the “hierarchical unit-cell template method,” is an interpretable
machine-learning approach that uncovers global unit-cell shape/topology
patterns corresponding to predefined band-gap objectives. A scale-separation
effect is observed where the coarse-scale band-gap objective is mostly
unaffected by the fine-scale features despite the closeness of their length
scales, thus enabling an efficient hierarchical algorithm. Moreover, the
hierarchical patterns revealed are not predefined or self-similar hierarchies
as common in current hierarchical phononic materials. Thus, our approach offers
a flexible and efficient method for the exploration of new regions in the
hierarchical design space, extracting minimal effective patterns for inverse
design in applications targeting multiple frequency ranges.

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