Identifying structure–absorption relationships and predicting absorption strength of non-fullerene acceptors for organic photovoltaics. Issue 7 (26th May 2022)

• Record Type: Journal Article
• Authors: Yan, Jun; Rodríguez-Martínez, Xabier; Pearce, Drew; Douglas, Hana; Bili, Danai; Azzouzi, Mohammed; Eisner, Flurin; Virbule, Alise; Rezasoltani, Elham; Belova, Valentina; Dörling, Bernhard; Few, Sheridan; Szumska, Anna A.; Hou, Xueyan; Zhang, Guichuan; Yip, Hin-Lap; Campoy-Quiles, Mariano; Nelson, Jenny
• Is Part Of: Energy & environmental science. Volume 15:Issue 7(2022)
• Page Start: 2958
• Page End: 2973
• Publication Date: 2022-05-26
• Subjects: Energy conversion -- Periodicals; Fuel switching -- Periodicals; Environmental sciences -- Periodicals; Environmental chemistry -- Periodicals; 333.79
• DOI: 10.1039/d2ee00887d
• Languages: English
• ISSNs: 1754-5692

Abstract

Abstract : We combine experiments with density functional theory calculations, statistical analysis, and machine-learning to reveal the structure–absorption strength relationship and predict the absorption strength of organic non-fullerene acceptors. Abstract : Non-fullerene acceptors (NFAs) are excellent light harvesters, yet the origin of their high optical extinction is not well understood. In this work, we investigate the absorption strength of NFAs by building a database of time-dependent density functional theory (TDDFT) calculations of ∼500 π-conjugated molecules. The calculations are first validated by comparison with experimental measurements in solution and solid state using common fullerene and non-fullerene acceptors. We find that the molar extinction coefficient ( ε d, max ) shows reasonable agreement between calculation in vacuum and experiment for molecules in solution, highlighting the effectiveness of TDDFT for predicting optical properties of organic π-conjugated molecules. We then perform a statistical analysis based on molecular descriptors to identify which features are important in defining the absorption strength. This allows us to identify structural features that are correlated with high absorption strength in NFAs and could be used to guide molecular design: highly absorbing NFAs should possess a planar, linear, and fully conjugated molecular backbone with highly polarisable heteroatoms. We then exploit a random decision forest algorithm to draw predictions for ε d, max using a computational framework based on extended tight-binding Hamiltonians, which shows reasonable predicting accuracy with lower computational cost than TDDFT. This work provides a general understanding of the relationship between molecular structure and absorption strength in π-conjugated organic molecules, including NFAs, while introducing predictive machine-learning models of low computational cost.