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Predicting the photocurrent–composition dependence in organic solar cells

descriptionPublicationkeyboard_double_arrow_right Article , Journal 01 Jan 2021 Spain, Spain Publisher:Royal Society of Chemistry (RSC)Journal:Energy & Environmental Science, volume 14, pages 986-994 (issn: 1754-5692, eissn: 1754-5706,

Authors: Xabier Rodríguez-Martínez; Enrique Pascual-San-José; Zhuping Fei; Martin Heeney; Roger Guimerà; Mariano Campoy-Quiles;

doi: 10.1039/d0ee02958k

handle: 10261/226741

Predicting the photocurrent–composition dependence in organic solar cells

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Abstract

High-throughput experimental screening and machine-learning algorithms are implemented in a synergic workflow to predict the photocurrent phase space of organic photovoltaic blends. We identify accurate models employing only the materials band gaps.

Countries

Spain, Spain

Related Organizations

Spanish National Research Council
Spain
Universitat Rovira i Virgili
Spain
Institució Catalana de Recerca i Estudis Avançats
Spain
Institut de Ciència de Materials de Barcelona
Spain
"UNIVERSITAT ROVIRA I VIRGILI
Spain

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Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	29
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%