气候变化领域集成服务门户(CCPortal): Predicting the photocurrent-composition dependence in organic solar cells

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DOI	10.1039/d0ee02958k
	Predicting the photocurrent-composition dependence in organic solar cells
	Rodríguez-Martínez X.; Pascual-San-José E.; Fei Z.; Heeney M.; Guimerà R.; Campoy-Quiles M.
发表日期	2021
ISSN	17545692
起始页码	986
结束页码	994
卷号	14 期号:2
英文摘要	The continuous development of improved non-fullerene acceptors and deeper knowledge of the fundamental mechanisms governing performance underpin the vertiginous increase in efficiency witnessed by organic photovoltaics. While the influence of parameters like film thickness and morphology are generally understood, what determines the strong dependence of the photocurrent on the donor and acceptor fractions remains elusive. Here we approach this problem by training artificial intelligence algorithms with self-consistent datasets consisting of thousands of data points obtained by high-throughput evaluation methods. Two ensemble learning methods are implemented, namely a Bayesian machine scientist and a random decision forest. While the former demonstrates large descriptive power to complement the experimental high-throughput screening, the latter is found to predict with excellent accuracy the photocurrent-composition phase space for material systems outside the training set. Interestingly, we identify highly predictive models that only employ the materials band gaps, thus largely simplifying the rationale of the photocurrent-composition space. © The Royal Society of Chemistry.
英文关键词	Artificial intelligence; Energy gap; Learning systems; Organic solar cells; Phase space methods; Predictive analytics; Artificial intelligence algorithms; Composition dependence; Continuous development; Donor and acceptor; Fundamental mechanisms; High throughput screening; Organic photovoltaics; Strong dependences; Photocurrents; accuracy assessment; detection method; efficiency measurement; energy efficiency; fuel cell; model; perforation; performance assessment
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/190771
作者单位	Institut de Ciència de Materials de Barcelona, ICMAB-CSIC Campus Uab, Bellaterra, 08193, Spain; Department of Chemistry and Centre for Plastic Electronics, White City Campus, Imperial College London, London, W12 0BZ, United Kingdom; Institució Catalana de Recerca i Estudis Avançats, Icrea, Passeig de Lluís Companys 23, Barcelona, 08010, Spain; Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, 43007, Spain
推荐引用方式 GB/T 7714	Rodríguez-Martínez X.,Pascual-San-José E.,Fei Z.,et al. Predicting the photocurrent-composition dependence in organic solar cells[J],2021,14(2).
APA	Rodríguez-Martínez X.,Pascual-San-José E.,Fei Z.,Heeney M.,Guimerà R.,&Campoy-Quiles M..(2021).Predicting the photocurrent-composition dependence in organic solar cells.Energy & Environmental Science,14(2).
MLA	Rodríguez-Martínez X.,et al."Predicting the photocurrent-composition dependence in organic solar cells".Energy & Environmental Science 14.2(2021).