Nonfullerene Acceptors for Organic Photovoltaics: From Conformation Effect to Power Conversion Efficiencies Prediction. Issue 11 (31st July 2019)

• Record Type: Journal Article
• Authors: Sui, Ming-Yue; Yang, Zi-Rui; Geng, Yun; Sun, Guang-Yan; Hu, LiHong; Su, Zhong-Min
• Is Part Of: Solar RRL. Volume 3:Issue 11(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-31
• Subjects: bistricyclic aromatic enes -- conformation effects -- density functional theory -- nonfullerene acceptors -- power conversion efficiencies prediction; Solar energy -- Periodicals; Photovoltaic power generation -- Periodicals; Solar energy -- Research -- Periodicals; Photovoltaic power generation -- Research -- Periodicals; Periodicals; 333.7923
• DOI: 10.1002/solr.201900258
• Languages: English
• ISSNs: 2367-198X

Abstract

Abstract : Theoretical predictions of macroscopic performance (power conversion efficiencies [PCEs]) and experimental analyses for microscopic material (conformation) have always urged for organic photovoltaics. A series of acceptors based on multi‐conformation bistricyclic aromatic enes core have been designed. The results suggested that A4‐2, A5‐2, and T4‐2 show the full folded conformation, fitting, and exhibiting advantageous properties of various parts for acceptors effectively, thus getting high V OC and J SC ( k CS / k CR exceeds 10 12 ) as well. Their PCEs of devices matching different donors were predicted through machine learning (ML). In traditional device structures and crude environments, a maximum PCE is about seven times higher than original. Herein, a comprehensive investigation, ranging for conformations → donor/acceptor interfaces → morphology → PCEs, is carried out by pure theoretical methods. Therefore, this quantitative micro‐analysis combined with the ML intelligent prediction leads to a new approach in the development of the next generation of nonfullerene acceptors. Abstract : The bistricyclic aromatic enes molecules with multiple conformations are selected to further explore the effect of conformation on performance systematically. Then, a machine learning model is used to screen the more matched donor and predict the energy conversion efficiency of the device. A route from microscopic conformation to macroscopic performance design and characterization for organic photovoltaic device is established.