Articles producció científica> Enginyeria Química

Predicting the photocurrent-composition dependence in organic solar cells

  • Identification data

    Identifier: imarina:9173272
    Authors:
    Rodriguez-Martinez, XabierPascual-San-Jose, EnriqueFei, ZhupingHeeney, MartinGuimera, RogerCampoy-Quiles, Mariano
    Abstract:
    © 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.
  • Others:

    Author, as appears in the article.: Rodriguez-Martinez, Xabier; Pascual-San-Jose, Enrique; Fei, Zhuping; Heeney, Martin; Guimera, Roger; Campoy-Quiles, Mariano
    Department: Enginyeria Química
    URV's Author/s: Guimera Manrique, Roger
    Abstract: © 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.
    Thematic Areas: 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
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: roger.guimera@urv.cat
    Author identifier: 0000-0002-3597-4310
    Record's date: 2024-10-26
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://pubs.rsc.org/en/content/articlelanding/2021/ee/d0ee02958k#!divAbstract
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Energy & Environmental Science. 14 (2): 986-994
    APA: Rodriguez-Martinez, Xabier; Pascual-San-Jose, Enrique; Fei, Zhuping; Heeney, Martin; Guimera, Roger; Campoy-Quiles, Mariano (2021). Predicting the photocurrent-composition dependence in organic solar cells. Energy & Environmental Science, 14(2), 986-994. DOI: 10.1039/d0ee02958k
    Article's DOI: 10.1039/d0ee02958k
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2021
    Publication Type: Journal Publications
  • Keywords:

    Chemistry, Multidisciplinary,Energy & Fuels,Engineering, Chemical,Environmental Chemistry,Environmental Sciences,Nuclear Energy and Engineering,Pollution,Renewable Energy, Sustainability and the Environment
    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
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