Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning
This provides a flexible and efficient method for inverse design in applications like high-precision instruments, though it is incremental as it builds on existing hierarchical phononic materials with a novel machine-learning approach.
The researchers tackled the problem of designing hierarchical phononic materials with targeted band gaps for manipulating vibrational waves, achieving a scale-separation effect where coarse-scale band-gap objectives remain unaffected by fine-scale features, enabling efficient exploration of new design regions.
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.