Autor según el artículo: Rodríguez-Martínez X; Pascual-San-José E; Fei Z; Heeney M; Guimerà R; Campoy-Quiles M

Departamento: Enginyeria Química

Autor/es de la URV: Guimera Manrique, Roger

Resumen: © The Royal Society of Chemistry. 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.

Áreas temáticas: Renewable energy, sustainability and the environment Química Pollution Nuclear energy and engineering Materiais Environmental sciences Environmental chemistry Engineering, chemical Engenharias iv Engenharias ii Engenharias i Energy & fuels Chemistry, multidisciplinary Biotecnología Astronomia / física

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

Direcció de correo del autor: roger.guimera@urv.cat

Identificador del autor: 0000-0002-3597-4310

Fecha de alta del registro: 2023-02-19

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://pubs.rsc.org/en/content/articlelanding/2021/ee/d0ee02958k#!divAbstract

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Energy & Environmental Science. 14 (2): 986-994

Referencia de l'ítem segons les normes 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), 986-994. DOI: 10.1039/d0ee02958k

DOI del artículo: 10.1039/d0ee02958k

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2021

Tipo de publicación: Journal Publications