Prediction of power conversion efficiency parameter of inverted organic solar cells using artificial intelligence techniques

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F24%3A10256190" target="_blank" >RIV/61989100:27730/24:10256190 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41598-024-77112-3" target="_blank" >https://www.nature.com/articles/s41598-024-77112-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-024-77112-3" target="_blank" >10.1038/s41598-024-77112-3</a>

Result language

angličtina

Original language name

Prediction of power conversion efficiency parameter of inverted organic solar cells using artificial intelligence techniques

Original language description

Organic photovoltaic (OPV) cells are at the forefront of sustainable energy generation due to their lightness, flexibility, and low production costs. These characteristics make OPVs a promising solution for achieving sustainable development goals. However, predicting their lifetime remains challenging task due to complex interactions between internal factors such as material degradation, interface stability, and morphological changes, and external factors like environmental conditions, mechanical stress, and encapsulation quality. In this study, we propose a machine learning-based technique to predict the degradation over time of OPVs. Specifically, we employ multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to predict the power conversion efficiency (PCE) of inverted organic solar cells (iOSCs) made from the blend PTB7-Th:PC70BM, with PFN as the electron transport layer (ETL), fabricated under an N2 environment. We evaluate the performance of the proposed technique using several statistical metrics, including mean squared error (MSE), root mean squared error (rMSE), relative squared error (RSE), relative absolute error (RAE), and the correlation coefficient (R). The results demonstrate the high accuracy of our proposed technique, evidenced by the minimal error between predicted and experimentally measured PCE values: 0.0325 for RSE, 0.0729 for RAE, 0.2223 for rMSE, and 0.0541 for MSE using the LSTM model. These findings highlight the potential of proposed models in accurately predicting the performance of OPVs, thus contributing to the advancement of sustainable energy technologies.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

<a href="/en/project/TN02000025" target="_blank" >TN02000025: National Centre for Energy II</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

—

Volume of the periodical

14

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

23

Pages from-to

1-23

UT code for WoS article

001345876000029

EID of the result in the Scopus database

2-s2.0-85208164564