The article "AI-Based Metamaterial Design" by Ece Tezsezen, Defne Yigci, Abdollah Ahmadpour, and Savas Tasoglu discusses the transformative impact of artificial intelligence (AI) on the design and application of metamaterials across various fields, including optics, acoustics, healthcare, and power systems. Metamaterials, which can control electromagnetic, thermal, and acoustic properties, have revolutionized these areas but face challenges in traditional design methods that are time-consuming and resource-intensive. AI-based design offers a solution by automating and accelerating the design process, enabling the exploration of a vast design space and the optimization of parameters that are difficult to achieve with traditional methods. The review highlights the use of machine learning (ML) algorithms, such as regression, probabilistic, and optimization algorithms, to generate and optimize metamaterial designs. Generative models like GANs and VAEs are particularly effective in creating novel metamaterial structures, while optimization algorithms like genetic algorithms and particle swarm optimization help in fine-tuning metamaterial architecture. In the context of acoustic metamaterials, AI-based design is transforming the field by improving sound absorption, noise control, and acoustic cloaking. AI models can predict acoustic performance, optimize designs, and analyze different variations, leading to more efficient and effective solutions. Challenges in manufacturing and scalability are addressed through the integration of AI models that prioritize manufacturability parameters. In optics, AI-based design is enhancing the performance of meta-lenses and meta-gratings. ML algorithms, such as NNs and CNNs, are used to accelerate the design process, reduce computational costs, and improve the accuracy of predictions. These advancements are crucial for applications in virtual reality, security encryption, and high-contrast microscopy. In healthcare, AI-based metamaterial design is being used to develop sensitive and accurate diagnostic sensors and point-of-care (POC) devices. Terahertz metamaterial absorbers (TMAs) and wearable sensors are examples of how AI can improve the detection of biomarkers and the monitoring of vital signs. The integration of AI with metamaterials is also enabling the development of label-free biosensors and electronic noses, which can provide noninvasive and continuous monitoring of patient health. Overall, the article emphasizes the potential of AI to revolutionize the design and application of metamaterials, addressing current challenges and opening new avenues for future research and innovation.The article "AI-Based Metamaterial Design" by Ece Tezsezen, Defne Yigci, Abdollah Ahmadpour, and Savas Tasoglu discusses the transformative impact of artificial intelligence (AI) on the design and application of metamaterials across various fields, including optics, acoustics, healthcare, and power systems. Metamaterials, which can control electromagnetic, thermal, and acoustic properties, have revolutionized these areas but face challenges in traditional design methods that are time-consuming and resource-intensive. AI-based design offers a solution by automating and accelerating the design process, enabling the exploration of a vast design space and the optimization of parameters that are difficult to achieve with traditional methods. The review highlights the use of machine learning (ML) algorithms, such as regression, probabilistic, and optimization algorithms, to generate and optimize metamaterial designs. Generative models like GANs and VAEs are particularly effective in creating novel metamaterial structures, while optimization algorithms like genetic algorithms and particle swarm optimization help in fine-tuning metamaterial architecture. In the context of acoustic metamaterials, AI-based design is transforming the field by improving sound absorption, noise control, and acoustic cloaking. AI models can predict acoustic performance, optimize designs, and analyze different variations, leading to more efficient and effective solutions. Challenges in manufacturing and scalability are addressed through the integration of AI models that prioritize manufacturability parameters. In optics, AI-based design is enhancing the performance of meta-lenses and meta-gratings. ML algorithms, such as NNs and CNNs, are used to accelerate the design process, reduce computational costs, and improve the accuracy of predictions. These advancements are crucial for applications in virtual reality, security encryption, and high-contrast microscopy. In healthcare, AI-based metamaterial design is being used to develop sensitive and accurate diagnostic sensors and point-of-care (POC) devices. Terahertz metamaterial absorbers (TMAs) and wearable sensors are examples of how AI can improve the detection of biomarkers and the monitoring of vital signs. The integration of AI with metamaterials is also enabling the development of label-free biosensors and electronic noses, which can provide noninvasive and continuous monitoring of patient health. Overall, the article emphasizes the potential of AI to revolutionize the design and application of metamaterials, addressing current challenges and opening new avenues for future research and innovation.