A Machine Learning Approach to Predict the Optical Properties of a Nanocube via Gaussian Process Regression

We have demonstrated the use of a Gaussian Process Regression method for the prediction of the scattering and extinction cross sections of a nanocube embedded in a dielectric medium. Our model is sufficiently general to incorporate permittivity of the cube and dielectric, cube size, and wavelength as predictors, resulting in a level of generality previously unseen in the literature. We introduce the idea of using the logarithms of the normalized cross sections during training, which improves the accuracy greatly. The model has been able to accurately predict cross sections for cube sizes and materials outside of the training set, in times that are orders of magnitude smaller than that required for a Finite Element Method simulation, thus demonstrating both the speed and predictive power of the simple machine learning approach considered in this work.