<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>