Prediction of optical spectra of BeZnO alloys using machine learning

The selection of materials with specific, desired optical properties is essential to improve optical and photonic devices. Specifically, BeZnO alloys have been a promising candidate due to their wide and tunable bandgaps. In order to represent the alloy, either cluster expansion methods are used, that are limited to small supercell sizes on the order of 10-20 atoms, or special quasirandom structures are used to describe random alloys by means of somewhat larger cell sizes. We explore whether machine learning can be used to accelerate the computation of optical spectra of large BeZnO alloy supercells. To understand the relationship between alloying and optical properties, structural descriptors are used as input for machine learning models. The optical spectra dataset is created by using density functional theory of several hundreds of representations from a cluster expansion. We train and apply random forest regression to our dataset. Evaluation of these models depends on the following metrics: mean absolute error, root mean squared error, and coefficient of determination. We use these models, once trained, to predict optical spectra of a larger number of cluster classes with high accuracy and at a lower computational cost.