Machine learning for high performance organic solar cells: current scenario and future prospects
Machine learning (ML) is a field of computer science that uses algorithms and techniques for automating solutions to complex problems that are hard to program using conventional programming methods. Owing to the chemical versatility of organic building blocks, a large number of organic semi-conducto...
Saved in:
| Published in | Energy & environmental science Vol. 14; no. 1; pp. 9 - 15 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Cambridge
Royal Society of Chemistry
01.01.2021
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Summary: | Machine learning (ML) is a field of computer science that uses algorithms and techniques for automating solutions to complex problems that are hard to program using conventional programming methods. Owing to the chemical versatility of organic building blocks, a large number of organic semi-conductors have been used for organic solar cells. Selecting a suitable organic semi-conductor is like searching for a needle in a haystack. Data-driven science, the fourth paradigm of science, has the potential to guide experimentalists to discover and develop new high-performance materials. The last decade has seen impressive progress in materials informatics and data science; however, data-driven molecular design of organic solar cell materials is still challenging. The data-analysis capability of machine learning methods is well known. This review is written about the use of machine learning methods for organic solar cell research. In this review, we have outlined the basics of machine learning and common procedures for applying machine learning. A brief introduction on different classes of machine learning algorithms as well as related software and tools is provided. Then, the current research status of machine learning in organic solar cells is reviewed. We have discussed the challenges in anticipating the data driven material design, such as the complexity metric of organic solar cells, diversity of chemical structures and necessary programming ability. We have also proposed some suggestions that can enhance the usefulness of machine learning for organic solar cell research enterprises.
In this review, current research status about the machine learning use in organic solar cell research is reviewed. We have discussed the challenges in anticipating the data driven material design. |
|---|---|
| Bibliography: | Jin-Liang Wang, received his PhD degree in College of Chemistry and Molecular Engineering, Peking University under the supervision of Prof. Jian Pei, from 2003-2008. From 2008 to 2012, he was a Postdoctoral research fellow in The University of Akron and The University of North Carolina at Chapel Hill. In 2013, he was awarded the junior thousand talent award and joined Beijing Institute of Technology as a full professor. His major is organic optoelectronic materials chemistry and his interests focus on synthesis of functional organic molecular materials for optoelectronic molecular devices and investigation of the relationship of chemical s tructure, computational analysis, optimization of film morphology and device performances. Asif Mahmood, received his PhD degree from the National Center for Nanoscience and Technology (NCNST), China. Currently, he is a Post-Doctoral at School of Chemistry and Chemical Engineering, Beijing Institute of Technology under the supervision of Prof. Jin-Liang Wang. His research interests include the design and synthesis of organic semi-conductor materials for organic solar cells and computational analysis of organic solar cells. |
| ISSN: | 1754-5692 1754-5706 |
| DOI: | 10.1039/d0ee02838j |