论文标题
识别结构吸收关系并预测有机光伏的非富勒烯受体的吸收强度
Identifying structure-absorption relationships and predicting absorption strength of non-fullerene acceptors for organic photovoltaics
论文作者
论文摘要
非富勒烯受体(NFAS)是出色的轻型收割者,但是这种高光学灭绝的起源尚不清楚。在这项工作中,我们通过构建约500个PI偶联分子的时间依赖性密度功能理论(TDDFT)计算的数据库来研究NFA的吸收强度。首先,使用常见的富勒烯和非富勒烯受体对液体和固态的实验测量进行比较来验证计算。我们发现,摩尔灭绝系数(ε_(d,max))显示了溶液中的计算与实验之间的合理一致,这突出了TDDFT在预测有机Pi偶联分子的光学性质方面的有效性。然后,我们基于分子描述子进行统计分析,以确定哪些特征对于定义吸收强度很重要。这使我们能够识别与NFA中与高吸收强度相关的结构特征,并且可以用于引导分子设计:高吸收的NFA应具有平面,线性和完全共轭的分子主链,并具有高度可极化的杂原子。然后,我们利用基于扩展的紧密结合汉密尔顿人的计算框架来利用一个随机的决策林来对ε_(D,MAX)进行预测,该计算框架比TDDFT较低的计算成本显示了合理的预测准确性。这项工作提供了对包括NFA在内的PI结合有机分子中分子结构与吸收强度之间的关系的一般理解,同时引入了低计算成本的预测机器学习模型。
Non-fullerene acceptors (NFAs) are excellent light harvesters, yet the origin of such high optical extinction is not well understood. In this work, we investigate the absorption strength of NFAs by building a database of time-dependent density functional theory (TDDFT) calculations of ~500 pi-conjugated molecules. The calculations are first validated by comparison with experimental measurements on liquid and solid state using common fullerene and non-fullerene acceptors. We find that the molar extinction coefficient (ε_(d,max)) shows reasonable agreement between calculation in vacuum and experiment for molecules in solution, highlighting the effectiveness of TDDFT for predicting optical properties of organic pi-conjugated molecules. We then perform a statistical analysis based on molecular descriptors to identify which features are important in defining the absorption strength. This allows us to identify structural features that are correlated with high absorption strength in NFAs and could be used to guide molecular design: highly absorbing NFAs should possess a planar, linear, and fully conjugated molecular backbone with highly polarisable heteroatoms. We then exploit a random decision forest to draw predictions for ε_(d,max) using a computational framework based on extended tight-binding Hamiltonians, which shows reasonable predicting accuracy with lower computational cost than TDDFT. This work provides a general understanding of the relationship between molecular structure and absorption strength in pi-conjugated organic molecules, including NFAs, while introducing predictive machine-learning models of low computational cost.