Data Cleansing and Sub‐Unit‐Based Molecular Description Enable Accurate Prediction of The Energy Levels of Non‐Fullerene Acceptors Used in Organic Solar Cells

• Published in: Advanced science Vol. 11; no. 17; pp. e2308652 - n/a
• Main Authors: Zhang, Ting, Yuk Lin Lai, Joshua, Shi, Mingzhe, Li, Qing, Zhang, Chen, Yan, He
• Format: Journal Article
• Language: English
• Published: Germany John Wiley & Sons, Inc 01.05.2024; John Wiley and Sons Inc; Wiley
• Subjects: data cleansing; non‐fullerene acceptors; organic solar cells; prediction of energy levels; data cleansing; organic solar cells; prediction of energy levels; non‐fullerene acceptors
• ISSN: 2198-3844; 2198-3844
• DOI: 10.1002/advs.202308652

Non‐fullerene acceptors (NFAs) have recently emerged as pivotal materials for enhancing the efficiency of organic solar cells (OSCs). To further advance OSC efficiency, precise control over the energy levels of NFAs is imperative, necessitating the development of a robust computational method for accurate energy level predictions. Unfortunately, conventional computational techniques often yield relatively large errors, typically ranging from 0.2 to 0.5 electronvolts (eV), when predicting energy levels. In this study, the authors present a novel method that not only expedites energy level predictions but also significantly improves accuracy , reducing the error margin to 0.06 eV. The method comprises two essential components. The first component involves data cleansing, which systematically eliminates problematic experimental data and thereby minimizes input data errors. The second component introduces a molecular description method based on the electronic properties of the sub‐units comprising NFAs. The approach simplifies the intricacies of molecular computation and demonstrates markedly enhanced prediction performance compared to the conventional density functional theory (DFT) method. Our methodology will expedite research in the field of NFAs, serving as a catalyst for the development of similar computational approaches to address challenges in other areas of material science and molecular research. An accurate and rapid computational method is developed to predict the energy levels of nonfullerene acceptors, yielding smaller prediction errors than previous computation methods. The accurate prediction is enabled by the combination of a data‐cleansing protocol that can effectively eliminate problematic experimental and a sub‐unit‐based molecular description method that greatly simplifies the complexity of molecular representations and hence the relevant molecular computation.