Skip to main navigation Skip to search Skip to main content

Machine Learning for Organic Photovoltaic Polymers: A Minireview

  • Asif Mahmood
  • , Ahmad Irfan
  • , Jin Liang Wang*
  • *Corresponding author for this work
  • Beijing Institute of Technology
  • King Khalid University

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish
Pages (from-to)870-876
Number of pages7
JournalChinese Journal of Polymer Science (English Edition)
Volume40
Issue number8
DOIs
Publication statusPublished - Aug 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Data science
  • Descriptors
  • Machine learning
  • Polymer solar cells
  • Polymers

Fingerprint

Dive into the research topics of 'Machine Learning for Organic Photovoltaic Polymers: A Minireview'. Together they form a unique fingerprint.

Cite this