The article "Unleashing the Potential: AI Empowered Advanced Metasurface Research" explores the integration of Artificial Intelligence (AI) with metasurfaces, a class of micro- and nano-optics that can manipulate light at the sub-wavelength scale. Metasurfaces, composed of meta-atom arrays, offer flexible control over amplitude, phase, and polarization, making them versatile for various applications such as imaging, communication, and display. However, the design of metasurfaces is a complex optimization problem with a large search space, often limited by computational resources and the complexity of Maxwell's equations. AI, particularly machine learning (ML), has emerged as a powerful tool to address these challenges. ML algorithms can process large datasets, extract complex patterns, and optimize metasurface designs more efficiently than traditional methods. The article reviews the basics of metasurfaces, including their design methods and applications, and introduces key AI concepts such as supervised and unsupervised learning. Supervised learning algorithms like Support Vector Machines (SVM), k-Nearest Neighbor (kNN), and Random Forest (RF) are used for classification and regression tasks in metasurface design. For example, SVM has been applied to predict amplitude curves and identify subwavelength-level unit features in metasurfaces. kNN is effective for optimizing structures in radiative cooling applications, while RF is suitable for designing patterned graphene metasurface absorbers. Unsupervised learning algorithms, such as genetic-type tree search (GTTS), are used for automatic inverse design of metasurfaces that enable high-directional beam steering. GTTS combines unsupervised clustering to group similar nanoscale antennas and virtual space concepts for efficient optimization. The article highlights the advantages of AI in metasurface research, including improved accuracy, efficiency, and the ability to handle complex, high-dimensional datasets. It also discusses the limitations of current research resources and the challenges posed by the interdisciplinary integration of AI and metasurface science. The conclusion emphasizes the potential of AI to enhance the performance and functionality of metasurfaces, making them a promising direction for future advancements in micro- and nano-optics.The article "Unleashing the Potential: AI Empowered Advanced Metasurface Research" explores the integration of Artificial Intelligence (AI) with metasurfaces, a class of micro- and nano-optics that can manipulate light at the sub-wavelength scale. Metasurfaces, composed of meta-atom arrays, offer flexible control over amplitude, phase, and polarization, making them versatile for various applications such as imaging, communication, and display. However, the design of metasurfaces is a complex optimization problem with a large search space, often limited by computational resources and the complexity of Maxwell's equations. AI, particularly machine learning (ML), has emerged as a powerful tool to address these challenges. ML algorithms can process large datasets, extract complex patterns, and optimize metasurface designs more efficiently than traditional methods. The article reviews the basics of metasurfaces, including their design methods and applications, and introduces key AI concepts such as supervised and unsupervised learning. Supervised learning algorithms like Support Vector Machines (SVM), k-Nearest Neighbor (kNN), and Random Forest (RF) are used for classification and regression tasks in metasurface design. For example, SVM has been applied to predict amplitude curves and identify subwavelength-level unit features in metasurfaces. kNN is effective for optimizing structures in radiative cooling applications, while RF is suitable for designing patterned graphene metasurface absorbers. Unsupervised learning algorithms, such as genetic-type tree search (GTTS), are used for automatic inverse design of metasurfaces that enable high-directional beam steering. GTTS combines unsupervised clustering to group similar nanoscale antennas and virtual space concepts for efficient optimization. The article highlights the advantages of AI in metasurface research, including improved accuracy, efficiency, and the ability to handle complex, high-dimensional datasets. It also discusses the limitations of current research resources and the challenges posed by the interdisciplinary integration of AI and metasurface science. The conclusion emphasizes the potential of AI to enhance the performance and functionality of metasurfaces, making them a promising direction for future advancements in micro- and nano-optics.