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Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors

机译：通过机器学习和改进的描述符来预测有机太阳能电池的效率

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To design efficient materials for organic photovoltaics (OPVs), it is essential to identify the largest number of parameters that control their properties and build a model using these parameters (known as descriptors) for the prediction of the power conversion efficiency (PCE). By constructing a dataset for 280 small molecule OPV systems, it is found that for all high-performing devices, frontier molecular orbitals of donor molecules are nearly degenerated and in such cases, orbitals other than just highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) are involved in exciton formation, exciton dissociation, and hole transport processes influencing the macroscopic properties of OPVs. Machine learning approaches, including random forest, gradient boosting, deep neural network are used to build models for the prediction of PCE using 13 important microscopic properties of organic materials as descriptors. Quite impressive performance of the gradient boosting model (Pearson's coefficient = 0.79) indicates that it can certainly be applied to high-throughput virtual screening of promising new donor molecules for high-efficiency OPVs.
机译：为了设计用于有机光伏（OPV）的高效材料，必须确定可控制其性能的最大数量的参数，并使用这些参数（称为描述符）建立模型以预测功率转换效率（PCE）。通过为280个小分子OPV系统构建数据集，发现对于所有高性能设备，施主分子的前沿分子轨道几乎都退化了，在这种情况下，除了最高占据分子轨道（HOMO）和最低未占据分子以外的轨道分子轨道（LUMO）参与激子形成，激子解离和影响OPV宏观性质的空穴传输过程。机器学习方法（包括随机森林，梯度提升，深度神经网络）用于建立模型，以有机材料的13个重要微观特性作为描述符来预测PCE。梯度增强模型的出色表现（皮尔森系数= 0.79）表明，该模型可以肯定地用于高通量虚拟筛选有希望的新供体分子以用于高效OPV。

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