基于机器学习的有机太阳能电池能级预测及分子设计

Machine-Learning-Based Energy Level Prediction and Molecular Design of Organic Solar Cells

作为分布式可再生能源关键组成部分的有机太阳能电池,其效率的主要限制因素是分子的最高占据分子轨道(HOMO)和最低未占据分子轨道(LUMO)之间的能级差异。为了能降低有机太阳能电池的制造成本,提高有机太阳能电池的能量转换效率,提出利用机器学习分析有机太阳能电池的能级,指导分子设计。首先,利用机器学习的高效性和成本效益,筛选出20个关键特征,以深入分析其如何影响光伏器件的性能。随后,构建了6种不同的预测模型,对比发现其中基于梯度提升的XGBT模型在预测有机太阳能电池性能方面表现最佳,其决定系数为0.8,并且其均方根误差仅为0.2。最后,利用该模型有效地预测了有机太阳能电池的性能,并且通过对HOMO与LUMO的深入分析,成功识别出两种影响有机太阳能电池能级的关键分子结构。

The main limiting factor for the efficiency of organic solar cells,a key component of distributed renewable energy,is the energy level difference between the highest occupied molecular orbital(HOMO)and lowest unoccupied molecular orbital(LUMO)of molecules.In view of a reduction of the manufacturing cost of organic solar cells and an improvement of their energy conversion efficiency,machine learning is used to analyze the energy levels of organic solar cells and guide the molecular design.Firstly,based on the high efficiency and cost-effectiveness of machine learning,20 key features are selected for a deeper analysis of how they affect the performance of photovoltaic devices. Subsequently, 6 different prediction models are constructed and compared. It is found that the XGBT model based on gradient boosting is characterized with the best performance in predicting the property of organic solar cells, with a coefficient of determination of 0.8 and a root mean square error of 0.2. Finally, the performance of organic solar cells can be effectively predicted by using this model, and through an in-depth analysis of HOMO and LUMO, two key molecular structures that affect battery energy levels are successfully identified.