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

SUBMITTER: Chen W 

PROVIDER: S-EPMC10161039 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature