2021/11/10 by Zhi Chen, Alexander Ogren, Chen, Zhi +7
Engineering · #Acoustic Wave Phenomena Research #Applied Physics (physics.app-ph) #Computational Engineering #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Machine Learning (cs.LG) #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2111.05949
openalex publication_date 2021/11/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.