Machine learning assisted prediction of charge transfer properties in organic solar cells by using morphology-related descriptors

Charge transfer and transport properties are crucial in the photophysical process of exciton dissociation and recombination at the donor/acceptor (D/A) interface. Herein, machine learning (ML) is applied to predict the charge transfer state energy (ECT) and identify the relationship between ECT and intermolecular packing structures sampled from molecular dynamics (MD) simulations on fullerene- and non-fullerene-based systems with different D/A ratios (RDA), oligomer sizes, and D/A pairs. The gradient boosting regression (GBR) exhibits satisfactory performance (r = 0.96) in predicting ECT with π-packing related features, aggregation extent, backbone of donor, and energy levels of frontier molecular orbitals. The charge transport property affected by π-packing with different RDA has also been investigated by space-charge-limited current (SCLC) measurement and MD simulations. The SCLC results indicate an improved hole transport of non-fullerene system PM6/Y6 with RDA of 1.2:1 in comparison with the 1:1 counterpart, which is mainly attributed to the bridge role of donor unit in Y6. The reduced energetic disorder is correlated with the improved miscibility of polymer with RDA increased from 1:1 to 1.2:1. The morphology-related features are also applicable to other complicated systems, such as perovskite solar cells, to bridge the gap between device performance and microscopic packing structures.