Organic photovoltaics (OPVs) have emerged as a promising alternative to conventional PV technology due to their low cost and industry-level scalability with high-volume production through solution-based processing. OPVs combine the unique flexibility and versatility of plastics with electronic properties, making them amenable to applications in the "Internet of Things" and distributed generation applications. The current key challenge for wide adaptation of OPVs is the lack of high power conversion efficiency (PCE) in large scale roll-to-roll processed devices. A key factor is the morphology: there exists disorder between the electron donor and electron acceptor materials in the active layer, and the mechanisms by which the morphology can be tuned are not well understood. Simulation is a promising inexpensive technique for exploring OPVs in the large parameter space of both processing methods and chemical components. In this work, we leverage and improve upon these computational approaches to reduce the need for iterative design for OPVs. First, we develop a multiscale molecular dynamics (MD) model to provide understanding of morphology evolution during solution processing. In addition, we train and utilize a predictive deep learning model to study the correlation of performance with the chemical and engineering design considerations. These parallel approaches allow for an accelerated sampling of the parameter space of OPV conditions, which in turn leads to targeted experiments.