气候变化领域集成服务门户(CCPortal): Machine learning-assisted development of organic photovoltaics via high-throughput in situ formulation

CCPortal

DOI	10.1039/d1ee00641j
	Machine learning-assisted development of organic photovoltaics via high-throughput in situ formulation
	An N.G.; Kim J.Y.; Vak D.
发表日期	2021
ISSN	17545692
起始页码	3438
结束页码	3446
卷号	14 期号:6
英文摘要	The discovery of high-performance non-fullerene acceptors and ternary blend systems has resulted in a breakthrough in the efficiency of organic photovoltaics (OPVs) and has created new opportunities for commercialization. However, manufacturing technology has remained far behind expectations. Here we show a new research approach to develop OPVsviaindustrial roll-to-roll (R2R) slot die coating in conjunction with thein situformulation technique and machine learning (ML) technology. The formulated PM6:Y6:IT-4F ternary blends deposited on continuously moving substrates resulted in the high-throughput fabrication of OPVs with various compositions. The system was used to produce training data for ML prediction. The composition/deposition parameters, referred to as deposition densities, and the efficiencies of 2218 devices were used to screen ML algorithms and to train an ML model based on a Random Forest regression algorithm. The generated model was used to predict high-performance formulations and the prediction was experimentally validated by fabricating 10.2% efficiency devices, the highest efficiency for R2R-processed OPVs so far. © The Royal Society of Chemistry 2021.
英文关键词	Decision trees; Efficiency; Forecasting; Machine learning; Polymer blends; Substrates; High throughput; Manufacturing technologies; Model-based OPC; Organic photovoltaics; Regression algorithms; Research approach; Slot-die coatings; Ternary blend systems; Engineering education; algorithm; coating; energy efficiency; machine learning; manufacturing; organic compound; performance assessment; photovoltaic system
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/190631
作者单位	Commonwealth Scientific and Industrial Research Organisation (CSIRO) Manufacturing, Clayton, Victoria 3168, Australia; School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea
推荐引用方式 GB/T 7714	An N.G.,Kim J.Y.,Vak D.. Machine learning-assisted development of organic photovoltaics via high-throughput in situ formulation[J],2021,14(6).
APA	An N.G.,Kim J.Y.,&Vak D..(2021).Machine learning-assisted development of organic photovoltaics via high-throughput in situ formulation.Energy & Environmental Science,14(6).
MLA	An N.G.,et al."Machine learning-assisted development of organic photovoltaics via high-throughput in situ formulation".Energy & Environmental Science 14.6(2021).