![]() We probed deeper into these networks’ architecture to explain how the optimized network could predict the final results. In addition, we addressed an issue related to DL methods, namely explainability. ![]() A combination of a CNN and MLP was designed to accept the cross-sectional images of cylindrical plasmonic core-shell nanomaterials as input and rapidly generate their optical response. Nanotubes of Si, Ge, and TiO 2 coated on either their inner wall or both their inner and outer walls with a plasmonic noble metal (Au or Ag) were thus modeled. Here, we used the combination of a computer vision technique, namely convolutional neural network (CNN), with multilayer perceptron (MLP) to obtain the far-field optical response at normal incidence (along cylinder axis) of concentric cylindrical plasmonic metastructures such as nanorods and nanotubes. The application domain of deep learning (DL) has been extended into the realm of nanomaterials, photochemistry, and optoelectronics research. ![]()
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