SDF-based inverse process design of solar cells using molecular dynamics simulations

Despite their lower weight, cheaper production cost, and diminished demand of metals, Organic Photovoltaic Cells (OPVCs) have not replaced conventional solar cells. The reason for this is their relatively low power conversion efficiency that could be a consequence of a lack in understanding of the underlying physics. More specifically, the influence of processing conditions and microstructure morphology on OPVC performance is still an open research area. In a previous study, we presented a spectral density function (SDF)-based design framework to optimize material performance with respect to its quasi-random microstructure (such as an OPVC). However, to guarantee manufacturability, the influence of processing conditions to material performance needs to be considered. In this study we present a two-step inverse design scheme which first identifies the optimal key microstructure descriptor(s) and then the optimal processing conditions. Dividing the design problem in two steps greatly benefits tractability as it allows the use of SDF reconstruction to reduce the dimensionality of the processing conditions. Subsequently, fewer costly high fidelity coarse-grained molecular dynamics simulations (or physical experiments) are required to identify the optimal processing conditions. We apply the introduced framework to optimize the performance of an OPVC with respect to volume fraction (i.e., chemical compositions) and annealing temperature. The inverse design approach results in a potential ten-fold decrease in the computational cost compared to direct process-performance optimization.