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Abstract：
Ternary polymer solar cells (PSCs) are currently the simplest and most efficient way to further improve the device performance in PSCs. To find high-performance organic photovoltaic materials, the established connection between the material structure and device performance before fabrication is of great significance. Herein, firstly, a database of the photovoltaic performance in 874 experimental PSCs reported in the literature is established, and three different fingerprint expressions of a molecular structure are explored as input features; the results show that long fingerprints of 2D atom pairs can contain more effective information and improve the accuracy of the models. Through supervised learning, five machine learning (ML) models were trained to build a mapping of the photovoltaic performance improvement relationship from binary to ternary PSCs. The GBDT model had the best predictive ability and generalization. Eighteen key structural features from a non-fullerene acceptor and the third components that affect the device\'s PCE were screened based on this model, including a nitrile group with lone-pair electron, a halogen atom, an oxygen atom, etc. Interestingly, the structural features for the enhanced device\'s PCE were essentially increased by the Jsc or FF. More importantly, the reliability of the ML model was further verified by preparing the highly efficient PSCs. Taking the PM6:BTP-eC9:PY-IT ternary PSC as an example, the PCE prediction (18.03%) by the model was in good agreement with the experimental results (17.78%), the relative prediction error was 1.41%, and the relative error between all experimental results and predicted results was less than 5%. These results indicate that ML is a useful tool for exploring the photovoltaic performance improvement of PSCs and accelerating the design and application with highly efficient non-fullerene materials.
摘要：
三元聚合物太阳能电池（PSC）是目前进一步提高PSC器件性能的最简单和最有效的方法。寻找高性能有机光伏材料,材料结构与器件性能之间的联系在制造前具有重要意义。在这里，首先,建立了文献中报道的874个实验PSC的光伏性能数据库，并探索了分子结构的三种不同指纹表达作为输入特征；结果表明，二维原子对的长指纹可以包含更有效的信息，并提高模型的准确性。通过监督学习，训练了五个机器学习（ML）模型，以构建从二元到三元PSC的光伏性能改善关系的映射。GBDT模型具有最好的预测能力和泛化性。基于该模型筛选了来自非富勒烯受体的18个关键结构特征和影响器件PCE的第三组分,包括带有孤对电子的腈基，卤素原子,一个氧原子,等。有趣的是,JSC或FF基本上增加了增强型设备PCE的结构特征。更重要的是,通过制备高效PSC,进一步验证了ML模型的可靠性。以PM6:BTP-eC9:PY-IT三元PSC为例，该模型的PCE预测(18.03%)与实验结果(17.78%)吻合良好，相对预测误差为1.41%，所有实验结果与预测结果的相对误差均小于5%。这些结果表明，ML是探索PSC光伏性能改善以及加速高效非富勒烯材料设计和应用的有用工具。
