Predicting the Absorption Spectra of Azobenzene Dyes

We have developed a Machine Learning (ML) framework for modeling the absorption spectra of azobenzene molecules—an important class of light-absorbing compounds with many current and potential applications. The ML models utilize predictors based on the structure and composition of each azobenzene molecule. Due to the relatively small size of the dataset (less than 500 molecule-spectrum pairs), dimensionality reduction of the original predictors is an important feature extraction step. With the reduced set of predictors, we trained separate regression models to predict the absorption at different wavelengths in the UV – visible light range. These models are able to accurately predict the absorption at fixed wavelengths, as well as the position and intensity of the maximum absorption. These predictions can be used to rapidly screen thousands of candidate molecules for a variety of potential applications, reducing the need for time-consuming and expensive experiments or quantum chemistry computations.