Machine learning-assisted designing of organic solar cell hole-transport molecules with promising short-circuit current density

Abstract

Organic solar cells (OSCs) have shown tremendous potential as a renewable energy source, but their efficiency is largely dependent on the design of the hole-transport layer. In this study, we use machine learning (ML) techniques to design and optimize organic donors for OSCs. A dataset of 940 small molecule donors (SMDs) was curated from peer-reviewed literature with experimental short-circuit voltage (Jsc) values. Among all regressors, the Random Forest achieves a high prediction accuracy for Jsc, while feature importance analysis reveals that MinAbsEStateIndex and fr_thiazole have a significant impact on the model. Leveraging the trained model, we design 1726 new SMDs with a high structure-activity landscape index (SALI) score of up to 9.6 to indicate their potential as efficient hole-transport materials. Further, t-distributed stochastic neighbour embedding and k-means clustering analysis is performed to identify patterns and clusters in the designed SMDs. This work demonstrates the power of ML in reducing computational and experimental costs associated with the design and optimization of SMDs for OSCs. By streamlining the design process, our approach can accelerate the development of more efficient OSCs, ultimately contributing to the advancement of renewable energy technologies.