Calibrating optical spectra using machine learning algorithms

Abstract

© 2020, Kazan Federal University. All rights reserved. We suggest an approach using machine learning random forest algorithms to comparing and calibrating the results of calculations of transition energies in organic molecules by ZINDO/S (Zerner’s intermediate neglect of differential overlap) and TDDFT (time-dependent densityfunctional theory) methods. We show how our machine learning model, trained on a relatively small data set can improve the results of semi-empirical methods and obtain absorption spectra comparable to TDDFT calculations.