Integration of Artificial Intelligence and Artificial Metamaterial

What is it about?

In 2024, the Nobel Prizes in Physics and Chemistry were awarded for fundamental discoveries and applications facilitated by artificial intelligence (AI). Originating from Turing's pioneering work, AI has undergone significant advancements over decades, encompassing the Turing test, expert systems, deep learning, and the iterative evolution of multimodal AI models. Metamaterials, artifical materials exhibiting electromagnetic properties beyond those found in nature, have demonstrated exceptional capabilities in complex electromagnetic control, proving indispensable for scientific exploration and wireless technology development. However, metamaterials with vast design parameters have posed significant challenges from traditional design methods, limiting their ability to meet the stringent efficiency requirements of practical applications. In recent years, the rapid advancement of AI, particularly deep learning, has revolutionized metamaterial design, significantly enhancing design efficiency and performance optimization. The integration of AI and artificial metamaterials has not only accelerated the design of multifunctional metamaterials but has also opened unprecedented avenues for innovation in electronics, optics, communications, radar, and sensing technologies. To address this exciting integration of AI and artificial metamaterials, Prof. Liming Si's research group at the School of Integrated Circuits and Electronics, Beijing Institute of Technology, has completed a significant review article titled "Advances in artificial intelligence for artificial metamaterials," which has been published in the renowned international journal APL Materials. This paper has been selected as a Featured Article and has been featured on the journal's front cover. Beijing Institute of Technology is the first affiliated institution, and Prof. Liming Si is the first and corresponding author.

Featured Image

Photo by Solen Feyissa on Unsplash

Photo by Solen Feyissa on Unsplash

Why is it important?

This paper provides a comprehensive overview of the diverse applications of artificial intelligence (AI) in metamaterial design, covering key stages such as forward prediction and inverse design. By using deep neural networks, researchers can rapidly predict the electromagnetic properties of metamaterials. Compared to traditional methods, the AI-based design strategy demonstrates superior performance in terms of design efficiency and accuracy, especially when dealing with complex structural designs and multi-dimensional parameter optimization. Furthermore, the paper showcases data-driven AI-generated model-based inverse design methods, such as transfer learning, generative adversarial networks, and variational autoencoders. These techniques, combined with large-scale datasets and efficient computational models, enable more complex and multi-objective design tasks. The paper also delves into electromagnetic wave physical neural networks, an innovative computational framework that leverages the diffraction characteristics of metamaterials to realize matrix operations and nonlinear activation functions, effectively supporting the execution of deep learning tasks. Compared to traditional electronic computing hardware, electromagnetic wave physical neural networks exhibit significant advantages in terms of speed, energy efficiency, and integration, offering a novel approach to addressing AI computational bottlenecks.

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