Machine learning assisted designing of organic semiconductors for organic solar cells: High-throughput screening and reorganization energy prediction

Abstract

Organic solar cells (OSCs) are ecofriendly and an inexpensive source of electricity production. However, high-throughput screening and designing new materials without performing trial-and-error experimental procedures is essential for the future commercialization of OSCs. Herein, a machine learning assisted approach is applied to design efficient organic semiconductors for OSCs in a fast and computationally cost-effective manner. Experimental and theoretical data from previous studies (databases) is collected for training of machine learning models to predict various properties of organic semiconductor materials such as reorganization energy. Moreover, high-throughput screening is performed to screen potential materials for OSCs. To evaluate the database's trends, data visualization analysis is performed. Moreover, Cook's distance is used to detect outliers in the machine learning models. Importantly, out of 22 tested models, only two models i.e., random forest regressor and extra trees regressor have shown better predictive capability. To check the applicability of this innovative approach, >1000 new organic semiconductors are designed by utilizing easily synthesizable organic building blocks. This machine learning approach can be used for high-throughput screening and designing of efficient materials for OSCs. © 2023 Elsevier B.V.