Machine learning using fingerprints and dye design in the search of lower hole reorganization energy.

Abstract

Organic solar cells (OSCs) are gaining fame for their cost-effective solution processing. Machine learning is increasingly popular for material design in OSCs. In this study, molecular fingerprints are used to train over 40 machine learning models. The random forest regressor emerges as the most predictive one. 10k new dyes are generated. A pre-trained ML model is used to predict their reorganization energy values. Dyes are selected on the basis of reorganization energy, dyes with lower reorganization energy are retrained. The synthetic accessibility of chosen dyes is then analyzed. Chemical similarity analysis has indicated reasonable resemble among selected dyes.