Organic solar cells capable of sustainably generating electricity are possible if: (1) The structures assembled by photoactive molecules can be controlled, and (2) The structures favorable for charge transport can be determined. In this dissertation we conduct computational studies to understand relationships between organic solar cell compounds, processing, structure and charge transport. We advance tools for encapsulating computational workflows so that simulations are more reproducible and transferable. We find that molecular dynamic simulations using simplified models efficiently predict experimental structures. We find that the mobilities of charges through these structures—as determined by kinetic Monte Carlo simulations—match qualitative trends expected with molecular ordering and in some cases agree quantitatively with experimental measurements. We identify percolating clusters with overlapping pi-orbitals as vital for fast charge transport, which are achieved through polymer tie-chains and extended molecular stacking. We find that machine learning predictions of electronic couplings from quantum chemical calculations gives a two-order-of-magnitude speed improvement relating structure to charge transport versus repeating the quantum calculations. We identify limitations of our structural and charge transport predictions, and provide recommendations for advancing future investigations of organic solar cells. In sum, the computational tools developed and employed herein enable the most broad and experimentally-validated sampling of self-assembled structure as a function of chemistry and processing to date. The fundamental understanding gained from these simulations informs the self-assembly and structure-transport relationships needed to advance organic solar cell engineering
