Simultaneous Optimization of Donor/acceptor Pairs and Device Specifications for Non-Fullerene Organic Solar Cells Using a QSPR Model with Morphological Descriptors

<p><a></a><a>In addition to designing new donor (D) and/or acceptor (A) molecules, the optimization of</a><a></a><a> experimental fabrication conditions </a>for the organic solar cells (OSCs) is also a complex, multidimensional challenge, which hasn’t been theoretically explored. Herein, a new framework for simultaneous optimizing D/A molecule pairs and device specifications of OSCs is proposed, through a quantitative structure-property relationships (QSPR) model built by machine learning. Combining the <a></a><a>device parameters</a> with<a></a><a> structural and electronic </a>variables, the built QSPR model achieved unprecedentedly high accuracy and consistency. Additionally, a huge chemical space containing <a>1,942,785</a> D/A pairs is explored to find potential synergistic ones. Favorable expereimental parameters such as root-mean-square (<i>RMS</i>) and the D/A ratio (<i>DAratio</i>) are further screened by grid search methods. <a></a><a></a><a></a><a>Overall, this study suggests </a>the feasibility to optimize D/A molecule pairs and device specifications simultaneously by enabling better-informed and data-driven techniques and this could facilitate the acceleration of improving OSCs efficiencies.</p>