Prediction of Intramolecular Reorganization Energy Using Machine Learning

Abstract

Facile charge transport is desired for many applications of organic semiconductors (OSCs). To take advantage of high-throughput screening methodologies for the discovery of novel OSCs, parameters relevant to charge transport are of high interest. The intramolecular reorganization energy (RE) is one of the important charge transport parameters suitable for molecular-level screening. Because the calculation of the RE with quantum-chemical methods is expensive for large-scale screening, we investigated the possibility of predicting the RE from the molecular structure by means of machine learning methods. We combinatorially generated a molecular library of 5631 molecules with extended conjugated backbones using benzene, thiophene, furan, pyrrole, pyridine, pyridazine, and cyclopentadiene as building blocks and obtained the target electronic data at the B3LYP level of theory with the 6-31G* basis set. We compared ridge, kernel ridge, and deep neural net (DNN) regression models based on graph- and geometry-based descriptors. We found that DNNs outperform the other methods and can predict the RE with a coefficient of determination of 0.92 and root-mean-square error of similar to 12 meV. This study shows that the REs of organic semiconductor molecules can be predicted from the molecular structures with high accuracy.