Organic Photovoltaics: Relating Chemical Structure, Local Morphology, and Electronic Properties

Robust computational methodologies that combine all-atom molecular dynamics simulations and density functional theory calculations allow a molecular-scale description of chemical structure–local morphology–electronic properties relationships. Packing of polymer chain segments on molecules at the nanoscale is found to depend on the strength of intermolecular interactions, the degree of molecular planarity, and the steric distribution of side chains. As an overall measure of intermolecular interactions present in a blend, the Flory–Huggins interaction parameter, χ, can be used to evaluate the degree of intermolecular mixing or the extent of phase separation. The reduced voltage losses measured in efficient polymer:NF-SMA systems can be attributed to higher energies of the charge-transfer electronic states, lower energetic disorders, and greater luminescence efficiency. Coarse-graining methods are expected to play a critical role in paving the way from local morphology to global morphology. 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. 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. trajectory that includes the coordinates of all of the atoms in a system as a function of simulation time. occur at the donor–acceptor interfaces and correspond to a full or partial electron transfer from an electron donor to an acceptor on photoexcitation. comprises a donor and an acceptor directly interacting within a given distance of each other; the cutoff distance along the interaction direction is based on the first-peak position present in the RDF. comprises two donor (acceptor) molecules directly interacting with each other. here, the focus is on the morphology-dependent electronic properties (e.g., site energies, electronic couplings among electronic states, energies of CT states, and their energetic distributions) involved in the exciton-dissociation, charge-separation, charge-recombination, and charge-transport processes. the extent of the phase separation and crystallinity, size, and purity of the domains at the micro- and macroscales. refers to the mixing between donor and acceptor components in a blend. refers to the donor/donor, donor/acceptor, and acceptor/acceptor packings. reference is made to two basic configurations – the face-on configuration, where two adjacent molecules pack in a face-to-face manner, and the edge-on configuration, where two neighboring molecules pack in an edge-to-face manner. relates to the preferential packing between the electron-rich or electron-poor moieties of donors or acceptors. intermolecular packing and mixing at the nanoscale. the RDF – or pair correlation function g(r) in a system of particles such as atoms, molecules, colloids, etc. – describes how density varies as a function of distance from a reference particle; if ρ is the average number density of particles, the local averaged density at a distance r is ρg(r).