湖南工业大学学报2024，Vol.38Issue(5)：33-39,7.DOI:10.3969/j.issn.1673-9833.2024.05.005
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
摘要
Abstract
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.关键词
机器学习/分布式新能源/有机太阳能电池/最高占据分子轨道/最低未占据轨道Key words
machine learning/distributed new energy/organic solar cell/highest occupied molecular orbital(HOMO)/lowest unoccupied molecular orbital(LUMO)分类
信息技术与安全科学引用本文复制引用
彭鑫裕,雷敏,赵潇捷,彭志嫣..基于机器学习的有机太阳能电池能级预测及分子设计[J].湖南工业大学学报,2024,38(5):33-39,7.基金项目
湖南省省市联合基金资助项目(2020JJ6071) （2020JJ6071）
