Organic polymers are pervasive in modern everyday life, and they have enabled advances in areas ranging from health care to computer technology. Recent observations suggest that hierarchical nanostructures form in semiconducting polymers and suggest the possibility of multi-dimensional transport pathways. This project aims to accelerate discovery of materials through feedback between computational and experimental results. The research team will develop highly efficient computational methodologies to predict processing methods and materials that lead to hierarchical 3D-transport pathways. A goal of the research is to develop new computational methodologies for massively parallel computations to take advantage of advances in computational hardware. New conjugated polymers will be designed with guidance from theory, and physical measurements will be made to benchmark the computational framework, in order to understand the evolution of structure during solution casting. Scalable models to understand the role of domain boundaries in charge transport in semiconducting polymers will be developed using structural maps from electron microscopy. Open source codes and large datasets for benchmarking computational studies of charge transport in semiconducting polymers are a focus of the research.