On the use of Machine Learning to Discover Novel Donor-Acceptor Pairs For Organic Photovoltaic Devices

The search for efficient organic photovoltaics materials is crucial for advancing solar energy technologies due to their potential for low-cost, lightweight, and flexible solar cells compared to traditional inorganic photovoltaics. In this study, we employed a machine learning (ML) approach to predict the key photovoltaic parameters, namely the open-circuit voltage (Voc), the short-circuit current density (Jsc) and the power conversion efficiency (PCE) of organic semiconductors used in the active-layer of organic solar cells. We trained our ML model on a comprehensive dataset of known donor-acceptor (D/A) pairs and their respective photovoltaic properties. Using this trained model, we generated and evaluated numerous novel (D/A) combinations and predicted their Voc, Jsc and PCE values. This high-throughput screening enabled us to identify promising (D/A) pairs that have not yet been explored in the literature. As a result, our findings demonstrate the power of machine learning in accelerating the discovery and optimization of new materials combinations for organic photovoltaics, potentially leading to more efficient and cost-effective solar cells, thus advancing the viability of sustainable energy solutions.