AI-driven emergence of frequency information non-uniform distribution
  via THz metasurface spectrum prediction
Xiaohua Xing Yuqi Ren Die Zou Qiankun Zhang Bingxuan Mao Jianquan Yao Deyi Xiong Shuang Zhang Liang Wu
Recently, artificial intelligence has been extensively deployed across
various scientific disciplines, optimizing and guiding the progression of
experiments through the integration of abundant datasets, whilst continuously
probing the vast theoretical space encapsulated within the data. Particularly,
deep learning models, due to their end-to-end adaptive learning capabilities,
are capable of autonomously learning intrinsic data features, thereby
transcending the limitations of traditional experience to a certain extent.
Here, we unveil previously unreported information characteristics pertaining to
different frequencies emerged during our work on predicting the terahertz
spectral modulation effects of metasurfaces based on AI-prediction. Moreover,
we have substantiated that our proposed methodology of simply adding
supplementary multi-frequency inputs to the existing dataset during the target
spectral prediction process can significantly enhance the predictive accuracy
of the network. This approach effectively optimizes the utilization of existing
datasets and paves the way for interdisciplinary research and applications in
artificial intelligence, chemistry, composite material design, biomedicine, and
other fields.