摘要： Substantial enhancements in the efficiencies of bulk-heterojunction (BHJ) organic solar cells (OSCs) have come from largely trial-and-error-based optimizations of the morphology of the active layers. Further improvements, however, require a detailed understanding of the relationships among chemical structure, morphology, electronic properties, and device performance. On the experimental side, characterization of the local (i.e., nanoscale) morphology remains challenging, which has called for the development of robust computational methodologies that can reliably address those aspects. In this review, we describe how a methodology that combines all-atom molecular dynamics (AA-MD) simulations with density functional theory (DFT) calculations allows the establishment of chemical structure–local morphology–electronic properties relationships. We also provide a brief overview of coarse-graining methods in an effort to bridge local to global (i.e., mesoscale to microscale) morphology. Finally, we give a few examples of machine learning (ML) applications that can assist in the discovery of these relationships.