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

HU Song; LIU Guohong; HE Ying; YAN Jiachen; CHEN Hanle; YAN Xiliang; YAN Bing

doi:10.13205/j.hjgc.202206024

Volume 40 Issue 6

Sep. 2022

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2022 > 40(6): 188-193

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

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

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

doi: 10.13205/j.hjgc.202206024

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

Received Date: 2022-02-23
Available Online: 2022-09-01
Publish Date: 2022-09-01

Abstract

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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References(23)

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