Deep learning framework accelerates spectral prediction for MXene solar absorbers

Researchers have developed a deep learning framework to accelerate the prediction of electromagnetic spectra for MXene-based solar absorbers. This framework utilizes transfer learning with a fine-tuned MobileNet version 2 model, a multi-channel spectral refinement module for enhanced feature extraction, and Savitzky-Golay smoothing to reduce noise. The proposed model significantly outperforms existing methods, achieving a low root mean squared error and high coefficient of determination, offering a computationally efficient alternative for nanophotonic design. AI

IMPACT This framework offers a computationally efficient method for spectral prediction in nanophotonic design, potentially speeding up the development of new solar absorber materials.

RANK_REASON Academic paper detailing a novel deep learning framework for spectral prediction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Hugging Face
• MobileNet version 2
• MXenes
• Savitzky-Golay Smoothing Filters
• Shujaat Khan

• paper
• infra

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