Reflective gradient metasurfaces are reported as flat, ultra-thin light focusers using a cross-resonator array with spatially varied geometric parameters atop a continuous gold ground plane spaced by a layer of SiO₂. The sub-wavelength cross-shaped building element offers polarization-independent performance and full 2π phase tuning range by varying its width and length, which is explained by an analytical model based on harmonically oscillating dipole antenna. With a radial phase gradient, a metasurface is demonstrated to function as a parabolic reflector at 1.47 μm wavelength with the measured efficiency of 44%. In addition, by elaborately engineering the planar distribution of different building elements, another two focusing reflectors are designed and experimentally verified to anomalously reflect and concentrate light along normal direction but with oblique incident angles of 30° and 60°, respectively.

Computational modeling is a crucial approach in material-related research for discovering new materials with superior properties. However, the high design flexibility in materials, especially in the realm of metamaterials where the sub-wavelength structure provides an additional degree of freedom in design, poses a formidable computational cost in various real-world applications. With the advent of big data, deep learning (DL) brings revolutionary breakthroughs in many conventional machine learning and pattern recognition tasks such as image classification. The accompanied data-driven modeling paradigm also provides transformative methodology shift in materials science, from trial-and-error routine to intelligent material discovery and analysis. This review systematically summarize the application of DL in material science, based on a model selection perspective for both natural materials and metamaterials. The review aims to uncover the logic behind data-model relation with emphasis on suitable data structures for different scenarios in the material study and the corresponding problem-solving DL model architectures.