The research of metamaterial shows great potential in the field of solar energy harvesting. In the past decade, the design of broadband solar metamaterial absorber (SMA) has attracted a surge of interest. The conventional design typically requires brute-force optimizations with a huge sampling space of structure parameters. Very recently, deep learning (DL) has provided a promising way in metamaterial design, but its application on SMA development is barely reported due to the complicated features of broadband spectrum. Here, this work develops the DL model based on metamaterial spectrum transformer (MST) for the powerful design of high-performance SMAs. The MST divides the optical spectrum of metamaterial into N patches, which overcomes the severe problem of overfitting in traditional DL and boosts the learning capability significantly. A flexible design tool based on free customer definition is developed to facilitate the real-time on-demand design of metamaterials with various optical functions. The scheme is applied to the design and fabrication of SMAs with graded-refractive-index nanostructures. They demonstrate the high average absorptance of 94% in a broad solar spectrum and exhibit exceptional advantages over many state-of-the-art counterparts. The outdoor testing implies the high-efficiency energy collection of about 1061 kW h m −2 from solar radiation annually. This work paves a way for the rapid smart design of SMA, and will also provide a real-time developing tool for many other metamaterials and metadevices.

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The selective broadband absorption of solar radiation plays a crucial role in applying solar energy. However, despite being a decade-old technology, the rapid and precise designs of selective absorbers spanning from the solar spectrum to the infrared region remain a significant challenge. This work develops a high-performance design paradigm that combines deep learning and multi-objective double annealing algorithms to optimize multilayer nanostructures for maximizing solar spectral absorption and minimum infrared radiation. Based on deep learning design, we experimentally fabricate the designed absorber and demonstrate its photothermal effect under sunlight. The absorber exhibits exceptional absorption in the solar spectrum (calculated/measured = 0.98/0.94) and low average emissivity in the infrared region (calculated/measured = 0.08/0.19). This absorber has the potential to result in annual energy savings of up to 1743 kW h/m2 in areas with abundant solar radiation resources. Our study opens a powerful design method to study solar-thermal energy harvesting and manipulation, which will facilitate for their broad applications in other engineering applications.