Singlet-Triplet Energy Gap as a Critical Molecular Descriptor for Predicting Organic Photovoltaic Efficiency

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 (ΔE_ST ) as a critical molecular descriptor for predicting the PCE considering that minimizing ΔE_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 ΔE_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.

Keywords: Energy Gap; Machine Learning; Singlet-Triplet; Solar Cells; Triplet Recombination.