基于端到端深度学习的有机光伏材料光电转化效率预测

胡松; 刘国红; 何英; 颜嘉晨; 陈寒乐; 闫希亮; 闫兵

doi:10.13205/j.hjgc.202206024

基于端到端深度学习的有机光伏材料光电转化效率预测

doi: 10.13205/j.hjgc.202206024

胡松^1,2,
刘国红¹,
何英¹,
颜嘉晨¹,
陈寒乐¹,
闫希亮^1, ,,
闫兵^1,2

1. 广州大学大湾区环境研究院珠江三角州水质安全与保护教育部重点实验室, 广州 510006;
2. 山东大学环境科学与工程学院, 山东青岛 266237

基金项目:

国家自然科学基金重点项目"纳米细胞效应的大数据构建及智能模拟预测"(22036002)

国家自然科学基金青年科学基金项目"基于原子尺度深度学习的纳塑料及其复合污染物构效关系和毒性预测研究"(22106025)

详细信息

作者简介:
胡松(1998-),男,硕士研究生,主要研究方向为计算毒理学。husong981101@163.com

通讯作者:
闫希亮(1991-),男,理学博士,讲师,主要研究方向为计算毒理学。yanxiliang1991@gzhu.edu.cn

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出版历程
- 收稿日期: 2022-02-23
- 网络出版日期: 2022-09-01
- 刊出日期: 2022-09-01

PREDICTION ON PHOTOELECTRIC CONVERSION EFFICIENCY OF ORGANIC PHOTOVOLTAIC MATERIALS USING END-TO-END DEEP LEARNING

1. Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Institute of Environmental Research at Greater Bay Area, Guangzhou University, Guangzhou 510006, China;
2. School of Environmental Science and Engineering, Shandong University, Qingdao 266237, China

摘要

摘要: 碳中和背景下,亟需开发高效清洁的新型能源,以减少对化石能源的依赖。有机光伏材料作为一种可将太阳能或其他光能直接转化为电能的材料,日益成为一种具有重大应用前景的低碳能源材料。在探索新的高性能有机光伏材料的过程中,机器学习虽然能够提高材料设计效率,但其预测能力极大受制于描述符的开发和选取。利用循环神经网络、卷积神经网络、图神经网络等算法,构建端到端的深度学习模型预测有机光伏材料光电转化效率,所建模型可直接从SMILES符号、分子图像、分子图网络中提取化合物结构信息,而无须人为开发和选取描述符。所得模型不仅能够较为准确地预测有机光伏材料的光电转化效率(其中最优模型五折交叉验证结果和测试集预测结果决定系数均>0.73),而且能够识别影响转化效率的关键结构特征。该研究结果可为新型环境功能材料设计提供理论参考。
- 碳中和 /
- 有机光伏材料 /
- 机器学习 /
- 环境功能新材料设计 /
- 低碳能源
Abstract: Under the context of carbon neutrality,there is an urgent need to develop high-efficiency and clean energy to reduce the dependence on petrochemical energy.As a material that can directly convert solar energy or other light energy into electrical energy,organic photovoltaic materials have become an increasingly promising low-carbon energy material with great application prospects.In the process of exploring high-performance organic photovoltaic materials,although machine learning can improve the efficiency of material design,its predictive ability is still greatly restricted by the development and selection of descriptors.In the present study,the authors applied recurrent neural network,convolutional neural network,and graph neural network to build end-to-end deep learning models to predict the photoelectric conversion efficiency,and the constructed models could directly extract chemical features from SMILES symbols,molecular images,and molecular graph networks without the need of descriptors calculation and selection.The resulted models could not only accurately predict the photoelectric conversion efficiency of organic photovoltaic materials (the optimal model obtained R²>0.73 for both 5-fold cross validation and external validation),but also identify the key structural features that affect the conversion efficiency.The results could provide theoretical guidance for the design of new environmental functional materials.
- carbon neutrality /
- organic photovoltaic materials /
- machine learning /
- design of novel environmental functional materials /
- low-carbon energy

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