How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning. (arXiv:2111.05949v2 [cs.LG] UPDATED)

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. …

arxiv machine machine learning patterns