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 withπ-packing related features,aggregation extent,backbone of donor,and energy levels of frontier molecular orbitals.The charge transport property affected byπ-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.