气候变化领域集成服务门户(CCPortal): Accelerating organic solar cell material's discovery: high-throughput screening andbig data

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DOI	10.1039/d1ee00559f
	Accelerating organic solar cell material's discovery: high-throughput screening andbig data
	Rodríguez-Martínez X.; Pascual-San-José E.; Campoy-Quiles M.
发表日期	2021
ISSN	17545692
起始页码	3301
结束页码	3322
卷号	14 期号:6
英文摘要	The discovery of novel high-performing materials such as non-fullerene acceptors and low band gap donor polymers underlines the steady increase of record efficiencies in organic solar cells witnessed during the past years. Nowadays, the resulting catalogue of organic photovoltaic materials is becoming unaffordably vast to be evaluated following classical experimentation methodologies: their requirements in terms of human workforce time and resources are prohibitively high, which slows momentum to the evolution of the organic photovoltaic technology. As a result, high-throughput experimental and computational methodologies are fostered to leverage their inherently high exploratory paces and accelerate novel materials discovery. In this review, we present some of the computational (pre)screening approaches performed prior to experimentation to select the most promising molecular candidates from the available materials libraries or, alternatively, generate molecules beyond human intuition. Then, we outline the main high-throuhgput experimental screening and characterization approaches with application in organic solar cells, namely those based on lateral parametric gradients (measuring-intensive) and on automated device prototyping (fabrication-intensive). In both cases, experimental datasets are generated at unbeatable paces, which notably enhancebig datareadiness. Herein, machine-learning algorithms find a rewarding application niche to retrieve quantitative structure-activity relationships and extract molecular design rationale, which are expected to keep the material's discovery pace up in organic photovoltaics. © The Royal Society of Chemistry 2021.
英文关键词	Energy gap; Learning algorithms; Machine learning; Molecular graphics; Organic polymers; Photovoltaic cells; Computational methodology; High throughput screening; Molecular candidates; Organic photovoltaic materials; Organic photovoltaics; Quantitative structure activity relationship; Screening approaches; Solar cell materials; Organic solar cells; automation; computer simulation; design; experimental study; fuel cell; machine learning; molecular analysis; 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/190635
作者单位	Institut de Ciència de Materials de Barcelona, ICMAB-CSIC, Campus UAB, Bellaterra, 08193, Spain
推荐引用方式 GB/T 7714	Rodríguez-Martínez X.,Pascual-San-José E.,Campoy-Quiles M.. Accelerating organic solar cell material's discovery: high-throughput screening andbig data[J],2021,14(6).
APA	Rodríguez-Martínez X.,Pascual-San-José E.,&Campoy-Quiles M..(2021).Accelerating organic solar cell material's discovery: high-throughput screening andbig data.Energy & Environmental Science,14(6).
MLA	Rodríguez-Martínez X.,et al."Accelerating organic solar cell material's discovery: high-throughput screening andbig data".Energy & Environmental Science 14.6(2021).