CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊
JST China 收录期刊

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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

胡松, 刘国红, 何英, 颜嘉晨, 陈寒乐, 闫希亮, 闫兵. 基于端到端深度学习的有机光伏材料光电转化效率预测[J]. 环境工程, 2022, 40(6): 188-193. doi: 10.13205/j.hjgc.202206024
引用本文: 胡松, 刘国红, 何英, 颜嘉晨, 陈寒乐, 闫希亮, 闫兵. 基于端到端深度学习的有机光伏材料光电转化效率预测[J]. 环境工程, 2022, 40(6): 188-193. doi: 10.13205/j.hjgc.202206024
HU Song, LIU Guohong, HE Ying, YAN Jiachen, CHEN Hanle, YAN Xiliang, YAN Bing. PREDICTION ON PHOTOELECTRIC CONVERSION EFFICIENCY OF ORGANIC PHOTOVOLTAIC MATERIALS USING END-TO-END DEEP LEARNING[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 188-193. doi: 10.13205/j.hjgc.202206024
Citation: HU Song, LIU Guohong, HE Ying, YAN Jiachen, CHEN Hanle, YAN Xiliang, YAN Bing. PREDICTION ON PHOTOELECTRIC CONVERSION EFFICIENCY OF ORGANIC PHOTOVOLTAIC MATERIALS USING END-TO-END DEEP LEARNING[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 188-193. 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
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

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

  • 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。
  • [1] LIU Q S, JIANG Y F, JIN K, et al. 18% efficiency organic solar cells[J]. Science Bulletin, 2020, 65(4):272-275.
    [2] LI C, ZHOU J D, SONG J L, et al. Non-fullerene acceptors with branched side chains and improved molecular packing to exceed 18% efficiency in organic solar cells[J]. Nature Energy, 2021, 6(6):605-613.
    [3] CAI Y, LI Y, WANG R, et al. A well-mixed phase formed by two compatible non-fullerene acceptors enables ternary organic solar cells with efficiency over 18.6%[J]. Advanced Materials, 2021, 33(33):e2101733.
    [4] YAN J C, YAN X L, HU S, et al. Comprehensive interrogation on acetylcholinesterase inhibition by ionic liquids using machine learning and molecular modeling[J]. Environmental Science&Technology, 2021, 55(21):14720-14731.
    [5] YAN X L, SEDYKH A, WANG W Y, et al. Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations[J]. Nature Communications, 2020, 11(1):2519.
    [6] YAN X L, ZHANG J, RUSSO D P, et al. Prediction of nano-bio interactions through convolutional neural network analysis of nanostructure images[J]. ACS Sustainable Chemistry&Engineering, 2020, 8(51):19096-19104.
    [7] NAGASAWA S, AL-NAAMANI E, SAEKI A. Computer-aided screening of conjugated polymers for organic solar cell:classification by random forest[J]. The Journal of Physical Chemistry Letters, 2018, 9(10):2639-2646.
    [8] SAHU H, RAO W N, TROISI A, et al. Toward predicting efficiency of organic solar cells via machine learning and improved descriptors[J]. Advanced Energy Materials, 2018, 8(24):1801032.
    [9] SUN W B, ZHENG Y J, YANG K, et al. Machine learning-assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials[J]. Science Advances, 2019,5(11):eaay4275.
    [10] HACHMANN J, OLIVARES-AMAYA R, ATAHAN-EVRENK S, et al. The harvard clean energy project:large-scale computational screening and design of organic photovoltaics on the world community grid[J]. Journal of Physical Chemistry Letters, 2011, 2(17):2241-2251.
    [11] SCHARBER M C, MVHLBACHER D, KOPPE M, et al. Design rules for donors in bulk-heterojunction solar cells-towards 10% energy-conversion efficiency[J]. Advanced Materials, 2006, 18(6):789-794.
    [12] SUN W B, LI M, LI Y, et al. The use of deep learning to fast evaluate organic photovoltaic materials[J]. Advanced Theory and Simulations, 2019, 2(1):1800116.
    [13] 张珂,冯晓晗,郭玉荣,等.图像分类的深度卷积神经网络模型综述[J].中国图象图形学报, 2021, 26(10):2305-2325.
    [14] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. Arxiv,2014,abs/1409.1556.
    [15] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017:2261-2269.
    [16] 杨丽,吴雨茜,王俊丽,等.循环神经网络研究综述[J].计算机应用, 2018, 38(增刊2):1-6,26.
    [17] CHUNG J, GULCEHRE C, CHO K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J]. Arxiv, 2014,sbs/1412.3555..
    [18] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780.
    [19] 毕常遥,袁晓彤.基于Adam局部优化的分布式近似牛顿深度学习模型训练[J].计算机应用与软件, 2021, 38(10):278-283.
    [20] 王健宗,孔令炜,黄章成,等.图神经网络综述[J].计算机工程, 2021, 47(4):12.
    [21] XIONG Z P, WANG D Y, LIU X H, et al. Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism[J]. Journal of Medicinal Chemistry, 2020, 63(16):8749-8760.
    [22] DAVID W. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules[J]. Journal of Chemical Information&Computer Sciences, 1988, 28(1):31-36.
    [23] BAGHER A M. Comparison of organic solar cells and inorganic solar cells[J]. International Journal of Sustainable and Green Energy, 2014, 3(3):53-58.
  • 加载中
计量
  • 文章访问数:  303
  • HTML全文浏览量:  60
  • PDF下载量:  6
  • 被引次数: 0
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

目录

    /

    返回文章
    返回