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Appropriate energy-level alignment in non-fullerene ternary organic solar cells (OSCs) can enhance the power conversion efficiencies (PCEs), due to the simultaneous improvement in charge generation/transportation and reduction in voltage loss. Seven machine-learning (ML) algorithms were used to build the regression and classification models based on energy-level parameters to predict PCE and capture high-performance material combinations, and random forest showed the best predictive capability. Furthermore, two sets of verification experiments were designed to compare the experimental and predicted results. The outcome elucidated that a deep lowest unoccupied molecular orbital (LUMO) of the non-fullerene acceptors can slightly reduce the open-circuit voltage ( ) but significantly improve short-circuit current density ( ), and, to a certain extent, the could be optimized by the slightly up-shifted LUMO of the third component in non-fullerene ternary OSCs. Consequently, random forest can provide an effective global optimization scheme and capture multi-component combinations for high-efficiency ternary OSCs.

Capture the high-efficiency non-fullerene ternary organic solar cells formula by machine-learning-assisted energy-level alignment optimization.

作者信息

Hao Tianyu, Leng Shifeng, Yang Yankang, Zhong Wenkai, Zhang Ming, Zhu Lei, Song Jingnan, Xu Jinqiu, Zhou Guanqing, Zou Yecheng, Zhang Yongming, Liu Feng

机构信息

School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules, In-situ Center for Physical Science, and Center of Hydrogen Science Shanghai Jiao Tong University, Shanghai 200240, P.R. China.

State Key Laboratory of Fluorinated Functional Membrane Materials and Dongyue Future Hydrogen Energy Materials Company, Zibo City, Shandong Province 256401, P.R. China.

出版信息

Patterns (N Y). 2021 Aug 18;2(9):100333. doi: 10.1016/j.patter.2021.100333. eCollection 2021 Sep 10.

在非富勒烯三元有机太阳能电池（OSC）中，适当的能级匹配可以提高功率转换效率（PCE），这是由于电荷产生/传输得到同步改善且电压损失降低。使用七种机器学习（ML）算法基于能级参数构建回归和分类模型，以预测PCE并捕捉高性能材料组合，随机森林表现出最佳预测能力。此外，设计了两组验证实验来比较实验结果和预测结果。结果表明，非富勒烯受体的深最低未占据分子轨道（LUMO）会略微降低开路电压（），但会显著提高短路电流密度（），并且在一定程度上，非富勒烯三元OSC中第三组分略微上移的LUMO可以优化。因此，随机森林可以为高效三元OSC提供有效的全局优化方案并捕捉多组分组合。