Multispectral camouflage technologies, especially in the most frequently-used visible and infrared (VIS-IR) bands, are in increasing demand for the ever-growing multispectral detection technologies. Nevertheless, the efficient design of proper materials and structures for VIS-IR camouflage is still challenging because of the stringent requirement for selective spectra in a large VIS-IR wavelength range and the increasing demand for flexible color and infrared signal adaptivity. Here, a material-informatics-based inverse design framework is proposed to efficiently design multilayer germanium (Ge) and zinc sulfide (ZnS) metamaterials by evaluating only ~1% of the total candidates. The designed metamaterials exhibit excellent color matching and infrared camouflage performance from different observation angles and temperatures through both simulations and infrared experiments. The present material informatics inverse design framework is highly efficient and can be applied to other multi-objective optimization problems beyond multispectral camouflage.
Wavelength-selective thermal emitters have been frequently adopted as a typical platform for emissivity engineering to achieve desired target emissivity spectra for broad applications such as thermal camouflage, radiative cooling, and gas sensing, etc. However, previous design methods fail to tackle the simultaneous design of both materials and structures, either fixing materials to design structures or fixing structures to select proper materials, hindering the establishment of a general design framework for emissivity engineering applicable across different applications. Herein, we employ the deep Q-learning network algorithm, a reinforcement learning method based on deep learning framework, to design multilayer wavelength-selective thermal emitters for a diverse range of applications, including thermal camouflage, radiative cooling and gas sensing. With magnetron sputtering, these emitters are fabricated and measured, validating the desired emissivity spectra with the designed ones. The main merits of the deep Q-learning algorithm include that it can 1) autonomously select suitable materials from a self-built material library and 2) autonomously optimize structures, thus realizing simultaneous optimization of materials and structures for various emissivity engineering applications. The present method is demonstrated to be feasible and efficient in designing multilayer wavelength-selective thermal emitters, offering a general framework for emissivity engineering and paving the way for efficient design of nonlinear optimization problems across various physical fields.
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