Compounding Meta‐Atoms into Metamolecules with Hybrid Artificial Intelligence Techniques

• Published in: Advanced materials (Weinheim) Vol. 32; no. 6; pp. e1904790 - n/a
• Main Authors: Liu, Zhaocheng, Zhu, Dayu, Lee, Kyu‐Tae, Kim, Andrew S., Raju, Lakshmi, Cai, Wenshan
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.02.2020
• Subjects: Artificial intelligence; Controllability; deep learning; Design optimization; Evolutionary algorithms; Hybrid systems; Inverse design; Machine learning; Materials science; nanophotonics; neural networks; System effectiveness; Wave fronts; nanophotonics; deep learning; evolutionary algorithms; neural networks
• ISSN: 0935-9648; 1521-4095; 1521-4095
• DOI: 10.1002/adma.201904790

Molecules composed of atoms exhibit properties not inherent to their constituent atoms. Similarly, metamolecules consisting of multiple meta‐atoms possess emerging features that the meta‐atoms themselves do not possess. Metasurfaces composed of metamolecules with spatially variant building blocks, such as gradient metasurfaces, are drawing substantial attention due to their unconventional controllability of the amplitude, phase, and frequency of light. However, the intricate mechanisms and the large degrees of freedom of the multielement systems impede an effective strategy for the design and optimization of metamolecules. Here, a hybrid artificial‐intelligence‐based framework consolidating compositional pattern‐producing networks and cooperative coevolution to resolve the inverse design of metamolecules in metasurfaces is proposed. The framework breaks the design of the metamolecules into separate designs of meta‐atoms, and independently solves the smaller design tasks of the meta‐atoms through deep learning and evolutionary algorithms. The proposed framework is leveraged to design metallic metamolecules for arbitrary manipulation of the polarization and wavefront of light. Moreover, the efficacy and reliability of the design strategy are confirmed through experimental validations. This framework reveals a promising candidate approach to expedite the design of large‐scale metasurfaces in a labor‐saving, systematic manner. A hybrid artificial‐intelligence‐based framework is developed to accelerate the discovery and inverse design of metasurfaces with spatially varying building blocks. The framework breaks the design of the metamolecules into separate designs of meta‐atoms, and independently solves the smaller design tasks of the meta‐atoms through deep learning and evolutionary algorithms.