Singlet‐Triplet Energy Gap as a Critical Molecular Descriptor for Predicting Organic Photovoltaic Efficiency. Issue 49 (9th November 2022)

• Record Type: Journal Article
• Authors: Han, Guangchao; Yi, Yuanping
• Is Part Of: Angewandte Chemie international edition. Volume 61:Issue 49(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-09
• Subjects: Energy Gap -- Machine Learning -- Singlet-Triplet -- Solar Cells -- Triplet Recombination; Chemistry -- Periodicals; 540
• DOI: 10.1002/anie.202213953
• Languages: English
• ISSNs: 1433-7851

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. Abstract : The singlet‐triplet energy gap (Δ E ST ) is proposed as a critical molecular descriptor to predict organic photovoltaic (OPV) efficiency. An unprecedented prediction accuracy is achieved from data‐driven machine learning when two conventional descriptors are combined, optical gap ( E g ) and driving force (Δ E DA ), enabling high‐throughput screening of efficient OPV materials.