Machine Learning for Organic Photovoltaic Polymers: A Minireview

Asif Mahmood; Ahmad Irfan; Jin-Liang Wang

doi:10.1007/s10118-022-2782-5

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Machine Learning for Organic Photovoltaic Polymers: A Minireview

REVIEW | Updated：2022-07-21

- Machine Learning for Organic Photovoltaic Polymers: A Minireview
- Machine Learning for Organic Photovoltaic Polymers: A Minireview
- Chinese Journal of Polymer Science 2022年40卷第8期页码：870-876
- Affiliations：
  
  a.Key Laboratory of Cluster Science of Ministry of Education, Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials, Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering in Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China
  b.Department of Chemistry, College of Science, King Khalid University, Abha 61413, Saudi Arabia
- Author bio：
  
  jinlwang@bit.edu.cn
- Funds：
  
  This work was financially supported by the National Natural Science Foundation of China (Nos. 21971014, 21672023 and 21950410533). Jin-Liang Wang was supported by BIT Teli Young Fellow Recruitment Program. The authors thank the Analysis & Testing Center, Beijing Institute of Technology, for the characterization. A. Irfan express appreciation to the Deanship of Scientific Research at King Khalid University Saudi Arabia through a research groups program under grant number RGP1/36/43.
- DOI：10.1007/s10118-022-2782-5
  中图分类号：
- 收稿：2022-04-02，
  
  修回：2022-4-24，
  
  录用：2022-4-26，
  
  网络首发：2022-06-21，
  
  纸质出版：2022-08-01
- Accepted：
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Mahmood, A.; Irfan, A.; Wang, J. L. Machine learning for organic photovoltaic polymers: a minireview. Chinese J. Polym. Sci. 2022, 40, 870–876

Asif Mahmood, Ahmad Irfan, Jin-Liang Wang. Machine Learning for Organic Photovoltaic Polymers: A Minireview[J]. Chinese Journal of Polymer Science, 2022, 40(8): 870-876.
Mahmood, A.; Irfan, A.; Wang, J. L. Machine learning for organic photovoltaic polymers: a minireview. Chinese J. Polym. Sci. 2022, 40, 870–876 DOI： 10.1007/s10118-022-2782-5.

Asif Mahmood, Ahmad Irfan, Jin-Liang Wang. Machine Learning for Organic Photovoltaic Polymers: A Minireview[J]. Chinese Journal of Polymer Science, 2022, 40(8): 870-876. DOI： 10.1007/s10118-022-2782-5.

摘要

Machine learning is a powerful tool that can guide the experimentalists to discover and develop new high-performance polymeric materials. In this mini-review

various case studies related to the use of machine learning for polymer solar cells research are discussed. Various challenges and opportunities are also discussed.

Abstract

Machine learning is a powerful tool that can provide a way to revolutionize the material science. Its use for the designing and screening of materials for polymer solar cells is also increasing. Search of efficient polymeric materials for solar cells is really difficult task. Researchers have synthesized and fabricated so many materials. Sorting the results and get feedback for further research requires an innovative approach. In this minireview

we provides brief introduction of machine learning. The importance of machine learning is also mentioned

and the application of machine learning for polymeric material design is discussed. The key challenges that are hindering the wide spread use of machine are discussed. Suggestions are also given to improve the use of data science. The predictions using machine learning maybe not highly accurate but it definitely better than no prediction at all.

关键词

Keywords

references

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