Compounding Meta‐Atoms into Metamolecules with Hybrid Artificial Intelligence Techniques
By Zhaocheng Liu, Dayu Zhu, Kyu-Tae Lee, Andrew S. Kim, L. Raju, Wenshan Cai. 2019
117 citations recorded by the source corpus.
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.
Subfields: Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Organic Chemistry
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In Atomic and Molecular Physics, and Optics
- L. Catherine Brinson
- Alexander C. Ogren
- Zhi Chen
- Cynthia Rudin
- Chiara Daraio
- Yongmin Liu
- Xianzhe Zhang
- Yihao Xu
In Electronic, Optical and Magnetic Materials
Citations in the corpus
- How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning (2021, methodology): “[17] Zhaocheng Liu, Dayu Zhu, Kyu-Tae Lee, Andrew S Kim, Lakshmi Raju, Wenshan Cai, Compounding meta-atoms into metamolecules with hybrid artificial intelligence techniques, Adv.”
- Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks (2021, background): “Recently, Z. Liu et al. published their work that integrated the genetic algorithm with ANNs [95].”