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.

Applied Science Research Center Applied Science Private University Amman 11931 Jordan

College of Engineering University of Business and Technology 21448 Jeddah Saudi Arabia

Department of Electrical Engineering College of Engineering Taif University Taif 21944 Saudi Arabia

Department of Electrical Engineering College of Engineering University of Hafr Albatin Hafr Al Batin 39524 Saudi Arabia

ENET Centre VSB Technical University of Ostrava Ostrava Czech Republic

Ethiopian Artificial Intelligence Institute PO Box 40782 Addis Ababa Ethiopia

Laboratory of Studies and Development of Semiconductor and Dielectric Materials LeDMaScD University Amar Telidji of Laghouat BP 37G Route of Ghardaïa Laghouat 03000 Algeria

Materials Science and Informatics Laboratory MSIL Ziane Achour University of Djelfa Road Moudjbara PO Box 3117 Djelfa 17000 Algeria

Processes Energy Environment and Electrical Systems National Engineering School of Gabès University of Gabès Gabes Tunisia

Telecommunication and Smart Systems Laboratory Faculty of Sciences and Technology Ziane Achour University Djelfa Algeria

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