Machine learning‐assisted performance analysis of organic photovoltaics
Abstract Although the power conversion efficiency of organic solar cells (OSCs) has been rapidly improved, there is still a lot of room for designing and developing new materials and their combinations to approach the efficiency limit. In this work, we establish a database of ∼100 bulk heterojunctio...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2024-12-01
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| Series: | Materials Genome Engineering Advances |
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| Online Access: | https://doi.org/10.1002/mgea.74 |
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| _version_ | 1841543276094554112 |
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| author | Sijing Zhong Jiayi Huang Hengyu Meng Zhuo Feng Qianyue Wang Zhenyu Huang Lijie Zhang Shiwei Li Weiyang Gong Yusen Huang Lei Ying Ning Li |
| author_facet | Sijing Zhong Jiayi Huang Hengyu Meng Zhuo Feng Qianyue Wang Zhenyu Huang Lijie Zhang Shiwei Li Weiyang Gong Yusen Huang Lei Ying Ning Li |
| author_sort | Sijing Zhong |
| collection | DOAJ |
| description | Abstract Although the power conversion efficiency of organic solar cells (OSCs) has been rapidly improved, there is still a lot of room for designing and developing new materials and their combinations to approach the efficiency limit. In this work, we establish a database of ∼100 bulk heterojunction OSCs composed of representative donors and acceptors reported in the literature, and train machine learning models to identify the efficiency potential of donor‐acceptor combinations. We find that the fully connected neural network achieves a Pearson coefficient of up to 0.88 for predicting the efficiency of OSCs with different combinations of donors and acceptors. We use sure independence screening and sparsifying method with feature analysis to analyze and evaluate the performance of OSCs. To prove the reliability and viability of the predictive model, we introduce the theoretical efficiency limits and confidence tests into the process, which provides a simple but reliable solution to quickly analyze and evaluate the potential of OSC materials and material combinations. |
| format | Article |
| id | doaj-art-bf37fef3f6ea4b5e8351ca3111b55df9 |
| institution | Kabale University |
| issn | 2940-9489 2940-9497 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Materials Genome Engineering Advances |
| spelling | doaj-art-bf37fef3f6ea4b5e8351ca3111b55df92025-01-13T15:15:31ZengWiley-VCHMaterials Genome Engineering Advances2940-94892940-94972024-12-0124n/an/a10.1002/mgea.74Machine learning‐assisted performance analysis of organic photovoltaicsSijing Zhong0Jiayi Huang1Hengyu Meng2Zhuo Feng3Qianyue Wang4Zhenyu Huang5Lijie Zhang6Shiwei Li7Weiyang Gong8Yusen Huang9Lei Ying10Ning Li11Institute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou ChinaInstitute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou ChinaInstitute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou ChinaInstitute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou ChinaInstitute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou ChinaInstitute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou ChinaInstitute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou ChinaInstitute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou ChinaInstitute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou ChinaInstitute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou ChinaInstitute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou ChinaInstitute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou ChinaAbstract Although the power conversion efficiency of organic solar cells (OSCs) has been rapidly improved, there is still a lot of room for designing and developing new materials and their combinations to approach the efficiency limit. In this work, we establish a database of ∼100 bulk heterojunction OSCs composed of representative donors and acceptors reported in the literature, and train machine learning models to identify the efficiency potential of donor‐acceptor combinations. We find that the fully connected neural network achieves a Pearson coefficient of up to 0.88 for predicting the efficiency of OSCs with different combinations of donors and acceptors. We use sure independence screening and sparsifying method with feature analysis to analyze and evaluate the performance of OSCs. To prove the reliability and viability of the predictive model, we introduce the theoretical efficiency limits and confidence tests into the process, which provides a simple but reliable solution to quickly analyze and evaluate the potential of OSC materials and material combinations.https://doi.org/10.1002/mgea.74machine learningmaterial combinationsorganic photovoltaicsperformance analysisreliability |
| spellingShingle | Sijing Zhong Jiayi Huang Hengyu Meng Zhuo Feng Qianyue Wang Zhenyu Huang Lijie Zhang Shiwei Li Weiyang Gong Yusen Huang Lei Ying Ning Li Machine learning‐assisted performance analysis of organic photovoltaics Materials Genome Engineering Advances machine learning material combinations organic photovoltaics performance analysis reliability |
| title | Machine learning‐assisted performance analysis of organic photovoltaics |
| title_full | Machine learning‐assisted performance analysis of organic photovoltaics |
| title_fullStr | Machine learning‐assisted performance analysis of organic photovoltaics |
| title_full_unstemmed | Machine learning‐assisted performance analysis of organic photovoltaics |
| title_short | Machine learning‐assisted performance analysis of organic photovoltaics |
| title_sort | machine learning assisted performance analysis of organic photovoltaics |
| topic | machine learning material combinations organic photovoltaics performance analysis reliability |
| url | https://doi.org/10.1002/mgea.74 |
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