Unified neural network model for predicting optical responses in gold nanostructures

In this paper, we present a deep neural network model capable of simultaneously predicting the optical transmittance spectra of three distinct gold nanostructures: nanodisks, truncated cones, and nanocones. Our model achieved a mean squared error loss as low as 0.0015. By systematically varying geometric parameters, we generated a comprehensive dataset to train the model, which captures complex dependencies between structure and optical response. A key finding is the redshift in resonance dip associated with increasing sidewall tilt. We further validated the model by comparing its predictions with experimental results, showing excellent agreement. This work demonstrates a powerful, data-driven approach for the rapid design and optimization of plasmonic nanostructures, opening new possibilities for nanophotonic applications.