  Machine learning models can assist with metamaterials design by approximating
computationally expensive simulators or solving inverse design problems.
However, past work has usually relied on black box deep neural networks, whose
reasoning processes are opaque and require enormous datasets that are expensive
to obtain. In this work, we develop two novel machine learning approaches to
metamaterials discovery that have neither of these disadvantages. These
approaches, called shape-frequency features and unit-cell templates, can
discover 2D metamaterials with user-specified frequency band gaps. Our
approaches provide logical rule-based conditions on metamaterial unit-cells
that allow for interpretable reasoning processes, and generalize well across
design spaces of different resolutions. The templates also provide design
flexibility where users can almost freely design the fine resolution features
of a unit-cell without affecting the user's desired band gap.
