  This study investigates the application of an artificial neural network to
predict the complex dielectric properties of granular catalysts commonly used
in microwave reaction chemistry. The study utilizes finite element
electromagnetic simulations and two-dimensional convolutional neural networks
to solve for a large solution space of varying dielectrics. This convolutional
neural network was trained using a supervised learning approach and a common
backpropagation. The frequency range of interest was between 0.1 to 13.5 GHz
with the real part of the dielectric constants ranging from 1 to 100 and the
imaginary part ranging from 0.0 to 0.2. The network was double validated using
experimental data collected from a coaxial airline. The model was demonstrated
to convert either experimental or computational derived scattering parameter to
complex permittivities. Moreover, the model eliminates the need for iterative
solutions that often have difficulty with the piecewise continuous nature of
frequency dependent scattering parameters.
