Optimizing Spectral Prediction in MXene-Based Metasurfaces Through Multi-Channel Spectral Refinement and Savitzky-Golay Smoothing

The prediction of electromagnetic spectra for MXene-based solar absorbers, where MXenes are a family of two-dimensional transition metal carbides and nitrides, is a computationally intensive task traditionally addressed using full-wave solvers. This study introduces an efficient deep learning framework incorporating transfer learning, multi-channel spectral refinement, and Savitzky-Golay smoothing to accelerate and enhance spectral prediction accuracy. The proposed architecture leverages a pretrained MobileNet version 2 model, fine-tuned to predict 102-point absorption spectra from ($64\times64$) metasurface designs. Additionally, the multi-channel spectral refinement module processes the feature map through multiple convolutional channels, enhancing feature extraction, while Savitzky-Golay smoothing mitigates high-frequency noise. Experimental evaluations demonstrate that the proposed model significantly outperforms baseline convolutional neural network and deformable convolutional neural network models, achieving an average root mean squared error of 0.0227, coefficient of determination ($R^2$) of 0.9563, and peak signal-to-noise ratio of 33.10 decibels. The proposed framework presents a scalable and computationally efficient alternative to conventional solvers, positioning it as a viable candidate for rapid spectral prediction in nanophotonic design workflows.