气候变化领域集成服务门户(CCPortal): Machine learning for high performance organic solar cells: Current scenario and future prospects

CCPortal

DOI	10.1039/d0ee02838j
	Machine learning for high performance organic solar cells: Current scenario and future prospects
	Mahmood A.; Wang J.-L.
发表日期	2021
ISSN	17545692
起始页码	90
结束页码	105
卷号	14 期号:1
英文摘要	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. © The Royal Society of Chemistry.
英文关键词	Computer programming; Data Science; Machine learning; Organic solar cells; Solar buildings; Analysis capabilities; Complex problems; Current research status; Future prospects; Machine learning methods; Materials informatics; Programming ability; Solar cell materials; Learning algorithms; algorithm; fuel cell; future prospect; machine learning; organic compound; performance assessment; scenario analysis; solar power
语种	英语
来源期刊	Energy & Environmental Science
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/190808
作者单位	Key Laboratory of Cluster Science of Ministry of Education, Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 100081, China
推荐引用方式 GB/T 7714	Mahmood A.,Wang J.-L.. Machine learning for high performance organic solar cells: Current scenario and future prospects[J],2021,14(1).
APA	Mahmood A.,&Wang J.-L..(2021).Machine learning for high performance organic solar cells: Current scenario and future prospects.Energy & Environmental Science,14(1).
MLA	Mahmood A.,et al."Machine learning for high performance organic solar cells: Current scenario and future prospects".Energy & Environmental Science 14.1(2021).