Recent advances of computational chemistry in organic solar cell research

Abstract

The precise design of organic photovoltaic materials and the control of morphology in the active layer are crucial for achieving high-performance organic solar cells (OSCs). However, it still remains difficult to fully obtain the intrinsic properties of organic photovoltaic materials, as well as the details of molecular stacking in disordered films and the evolution of the specific morphology of active layers, using traditional characterization methods, which hinders the screening of organic photovoltaic materials and understanding the structure–property relationship. Accordingly, computational chemistry provides a good method and plays a vital role in current scientific research. In this review, we first introduce the theoretical methods used in the recent study of OSCs, including density functional theory (DFT), time-dependent DFT (TD-DFT), all atomic molecular dynamics (AAMD) and coarse-grained molecular dynamics (CGMD). Then, the effects of the molecular structure on its conformation, frontier molecular orbital, ultraviolet-visible (UV-Vis) absorption spectrum, dipole moment, electrostatic potential, binding energy, stacking, and morphological evolution are discussed and analyzed. In addition, the application of machine learning (ML) in materials screening is briefly summarized. Finally, the intrinsic properties of OSCs are summarized based on the molecular structure and the future development and prospects of OSCs are analyzed to accelerate the efficiency over 20% in the near future.