A Deep Learning Approach to the Forward Prediction and Inverse Design of Plasmonic Metasurface Structural Color - Raw Data

Reflection spectra of PDMS - Al nanorod metamaterials were collected using LUMERICAL FDTD simulations. PDMS material properties were defined using a refractive index of 1.41 and Al material properties were defined using frequency selective permittivities from the handbook of Palik. A total of 4620 structures were simulated, sweeping the following dimension parameters:

• Aluminium thickness (t)
• Pillar height (h)
• Pillar diameter (d)

Reflectance spectra were converted into CIE 1931 chromaticity values (x,y). This dataset is comprehensive and allows for the development of deep learning models for the forward and inverse design of the given metamaterial structure as detailed in the associated manuscript. The associated manuscript and supporting documentation provide extensive details of data collection and processing methods.