光伏系统
单重态
有机太阳能电池
带隙
光伏
钙钛矿(结构)
材料科学
工作(物理)
单重态裂变
化学物理
光电子学
化学
物理
原子物理学
热力学
激发态
有机化学
生态学
生物
作者
Guangchao Han,Yuanping Yi
标识
DOI:10.1002/ange.202213953
摘要
Abstract In contrast to the inorganic and perovskite solar cells, organic photovoltaics (OPV) depend on a series of charge generation and recombination processes, which complicates molecular design to improve the power conversion efficiencies (PCEs). Herein, we first propose the singlet‐triplet energy gap (Δ E ST ) as a critical molecular descriptor for predicting the PCE considering that minimizing Δ E ST is beneficial to simultaneously reduce voltage loss and triplet recombination. Remarkably, the results from data‐driven machine learning verify that the prediction accuracy of the Δ E ST (Pearson's correlation coefficient r =0.72) is apparently superior to that of two commonly used molecular descriptors in OPV, i.e., the optical gap ( r =0.65) and the driving force ( r =0.53). Moreover, an impressive prediction accuracy of r =0.81 is achieved just by combining the three descriptors. This work paves the way toward rapid and precise screening of efficient OPV materials.
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