Broadband Solar Metamaterial Absorbers Empowered by Transformer‐Based Deep Learning

Abstract

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.

Designing Metasurfaces for Efficient Solar Energy Conversion

“…The proposed material exhibited promising water evaporation performance (maximum ∼76% solar-to-vapor efficiency at 15 Suns) and great potential for broader applications requiring fast heat transfer dynamics. In contrast with most of experimentally focused studies, a recent work presented an original method taking advantage of both the forward and inverse design approaches to design a multilayer metamaterial absorber (Figure f) . A customer-defined absorption spectrum was used as a target and “split” into different wavelength sections.…”

“…In contrast with most of experimentally focused studies, a recent work presented an original method taking advantage of both the forward and inverse design approaches to design a multilayer metamaterial absorber (Figure 10f). 228 A customer-defined absorption spectrum was used as a target and "split" into different wavelength sections. The inverse design was used to predict the thicknesses of the layers constituting the metamaterial, which were arranged by increasing the values of their real refractive index n. The forward design then confirmed the optical absorption spectrum of the simulated multilayer.…”

“…(f) Schematic of model employing forward and inverse design to engineer a multilayer metamaterial absorber (right: cross-sectional SEM image). Adapted with permission from ref . Copyright 2023 Author(s), licensed under a CC-BY Creative Commons Attribution 4.0 License.…”

Metasurface inverse designed by deep learning for quasi-entire terahertz wave absorption

“…Traditional investigations into metasurface absorbers have typically focused on absorption within specific wavelength bands, categorized into narrowband 29–31 and broadband absorption modes. 32–37 Broadband absorption, in contrast to its narrowband counterpart, holds broader potential applications. Consequently, researchers have pursued strategies to further enhance absorption bandwidth, with the prevailing approach involving the layering of multiple absorber materials.…”

Machine-Learning-Assisted Design of Multiband Terahertz Metamaterial Absorber

“…Additionally, these approaches can be computationally expensive and require extensive simulations and manual parameter adjustments. To overcome these issues, various machine learning techniques, including deep learning, have emerged as a powerful tool in the design and optimization of metamaterials Machine learning (ML) techniques offer various applications in the field of metamaterials, such as in the discovery and design of new metamaterial compositions and structures by predicting properties and optimizing material configurations …”