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 (E_CT) and identify the relationship between E_CT and intermolecular packing structures sampled from molecular dynamics (MD) simulations on fullerene- and non-fullerene-based systems with different D/A ratios (R_DA), oligomer sizes, and D/A pairs. The gradient boosting regression (GBR) exhibits satisfactory performance (r = 0.96) in predicting E_CT 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 R_DA 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 R_DA 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 R_DA 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.