Study of annealing induced nanoscale morphology change in organic solar cells with machine learning

Nanoscale morphology of the active layer in organic solar cells (OSCs) plays a significant role in affecting the overall performance of OSCs. In this work, by using the domain size to describe the morphology change with different annealing temperatures, the influence of annealing to nanoscale morphology in OSCs is intensively studied. Also, machine learning is utilized in investigating the relation between domain size and the annealing temperature. The realization of mapping the domain sizes to their annealing temperatures is significant in bridging the simulation work of OSCs to the actual fabrication process, indicating that the optimized domain sizes obtained through simulation could now be converted to actual annealing temperature in device fabrication.