Optical materials capable of dynamically manipulating electromagnetic waves are an emerging field in memories, optical modulators, and thermal management. Recently, their multispectral design preliminarily attracts much attention, aiming to enhance their efficiency and integration of functionalities. However, the multispectral manipulation based on these materials is challenging due to their ubiquitous wavelength dependence restricting their capacity to narrow wavelengths. In this article, we cascade multiple tunable optical cavities with selective-transparent layers, enabling a universal approach to overcoming wavelength dependence and establishing a multispectral platform with highly integrated functions. Based on it, we demonstrate the multispectral (ranging from 400 nm to 3 cm), fast response speed (0.9 s), and reversible manipulation based on a typical phase change material, vanadium dioxide. Our platform involves tandem VO₂-based Fabry–Pérot (F-P) cavities enabling the customization of optical responses at target bands independently. It can achieve broadband color-changing capacity in the visible region (a shift of ~60 nm in resonant wavelength) and is capable of freely switching between three typical optical models (transmittance, reflectance, and absorptance) in the infrared to microwave regions with drastic amplitude tunability exceeding 0.7. This work represents a state-of-art advance in multispectral optics and material science, providing a critical approach for expanding the multispectral manipulation ability of optical systems.

Colloidal photonic crystal (PC)-based anti-counterfeiting materials have been widely studied due to their inimitable structural colors and tunable photonic band gaps (PBGs) as well as their convenient identification methods. In this review, we summarize recent developments of colloidal PCs in the field of anti-counterfeiting from aspects of security strategies, design, and fabrication principles, and identification means. Firstly, an overview of the strategies for constructing PC anti-counterfeiting materials composed of variable color PC patterns, invisible PC prints, and several other PC anti-counterfeiting materials is presented. Then, the synthesis methods, working principles, security level, and specific identification means of these three types of PC materials are discussed in detail. Finally, the summary of strengths and challenges, as well as development prospects in the attractive research field, are presented.

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.