Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis
Solar cell research aims to improve power conversion efficiency (PCE). This field has an extensive body of literature on the Web of Science. For researchers, it is impossible to understand the development of the entire field comprehensively through traditional reading methods. Knowledge is recorded...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202400306 |
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| _version_ | 1832592607218237440 |
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| author | Qing Wang Zhixin Liu Shengda Zhao Yangjun Yan Xinyi Li Yajie Zhang Xinghua Zhang |
| author_facet | Qing Wang Zhixin Liu Shengda Zhao Yangjun Yan Xinyi Li Yajie Zhang Xinghua Zhang |
| author_sort | Qing Wang |
| collection | DOAJ |
| description | Solar cell research aims to improve power conversion efficiency (PCE). This field has an extensive body of literature on the Web of Science. For researchers, it is impossible to understand the development of the entire field comprehensively through traditional reading methods. Knowledge is recorded in the literature by text and numbers. Researchers acquire knowledge through literature surveying, text reading, and thinking. The conversion from text and numbers to knowledge can be automatically completed by machines, which can avoid path‐dependent perspectives. In this work, an intelligent machine learning method for literature structure delineation and information extraction is proposed. As an example, a knowledge base of organic solar cells (OSCs) is extracted including topic analysis of literature, numerical characteristics of performance, and material information. Seven major research directions of OSCs are identified. The correlations between key performance parameters, including PCE, short‐circuit current density (JSC), open‐circuit voltage (VOC), and fill factor (FF), are revealed from text mining. A donor–acceptor material map of PCE is constructed which provides a road map for OSCs, indicating the bottleneck of this field. Moreover, the method of machine intelligence developed here can be used in any other materials field, aiding a comprehensive understanding of the development quickly. |
| format | Article |
| id | doaj-art-e8ddad74eed949f596fe21693fcce860 |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-e8ddad74eed949f596fe21693fcce8602025-01-21T07:26:27ZengWileyAdvanced Intelligent Systems2640-45672025-01-0171n/an/a10.1002/aisy.202400306Researching Organic Solar Cells from the Perspective of Literature Big Data AnalysisQing Wang0Zhixin Liu1Shengda Zhao2Yangjun Yan3Xinyi Li4Yajie Zhang5Xinghua Zhang6School of Physical Sciences and Engineering Beijing Jiaotong University Beijing 100044 ChinaSchool of Physical Sciences and Engineering Beijing Jiaotong University Beijing 100044 ChinaSchool of Physical Sciences and Engineering Beijing Jiaotong University Beijing 100044 ChinaSchool of Science Xihua University Chengdu 610039 ChinaSchool of Physical Sciences and Engineering Beijing Jiaotong University Beijing 100044 ChinaCAS Key Laboratory of Nanosystem and Hierarchical Fabrication CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology Beijing 100190 ChinaSchool of Physical Sciences and Engineering Beijing Jiaotong University Beijing 100044 ChinaSolar cell research aims to improve power conversion efficiency (PCE). This field has an extensive body of literature on the Web of Science. For researchers, it is impossible to understand the development of the entire field comprehensively through traditional reading methods. Knowledge is recorded in the literature by text and numbers. Researchers acquire knowledge through literature surveying, text reading, and thinking. The conversion from text and numbers to knowledge can be automatically completed by machines, which can avoid path‐dependent perspectives. In this work, an intelligent machine learning method for literature structure delineation and information extraction is proposed. As an example, a knowledge base of organic solar cells (OSCs) is extracted including topic analysis of literature, numerical characteristics of performance, and material information. Seven major research directions of OSCs are identified. The correlations between key performance parameters, including PCE, short‐circuit current density (JSC), open‐circuit voltage (VOC), and fill factor (FF), are revealed from text mining. A donor–acceptor material map of PCE is constructed which provides a road map for OSCs, indicating the bottleneck of this field. Moreover, the method of machine intelligence developed here can be used in any other materials field, aiding a comprehensive understanding of the development quickly.https://doi.org/10.1002/aisy.202400306big dataliterature datamachine learningmaterial informationsolar cell databases |
| spellingShingle | Qing Wang Zhixin Liu Shengda Zhao Yangjun Yan Xinyi Li Yajie Zhang Xinghua Zhang Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis Advanced Intelligent Systems big data literature data machine learning material information solar cell databases |
| title | Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis |
| title_full | Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis |
| title_fullStr | Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis |
| title_full_unstemmed | Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis |
| title_short | Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis |
| title_sort | researching organic solar cells from the perspective of literature big data analysis |
| topic | big data literature data machine learning material information solar cell databases |
| url | https://doi.org/10.1002/aisy.202400306 |
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