Singlet‐Triplet Energy Gap as a Critical Molecular Descriptor for Predicting Organic Photovoltaic Efficiency
Guangchao Han, Yuanping Yi
Abstract
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.