Marzouglal Mustapha, Souahlia Abdelkerim, Bessissa Lakhdar, Mahi Djillali, Rabehi Abdelaziz, Alharthi Yahya Z, Bojer Amanuel Kumsa, Flah Aymen, Alharthi Mosleh M, Ghoneim Sherif S M
Laboratory of Studies and Development of Semiconductor and Dielectric Materials, LeDMaScD, University Amar Telidji of Laghouat, BP 37G Route of Ghardaïa, Laghouat, 03000, Algeria.
Telecommunication and Smart Systems Laboratory, Faculty of Sciences and Technology, Ziane Achour University, Djelfa, Algeria.
Sci Rep. 2024 Oct 29;14(1):25931. doi: 10.1038/s41598-024-77112-3.
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:PCBM, 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.
有机光伏（OPV）电池因其重量轻、柔韧性好和生产成本低，处于可持续能源发电的前沿。这些特性使有机光伏电池成为实现可持续发展目标的一个有前景的解决方案。然而，由于内部因素（如材料降解、界面稳定性和形态变化）与外部因素（如环境条件、机械应力和封装质量）之间的复杂相互作用，预测其寿命仍然是一项具有挑战性的任务。在本研究中，我们提出了一种基于机器学习的技术来预测有机光伏电池随时间的降解情况。具体而言，我们使用多层感知器（MLP）和长短期记忆（LSTM）神经网络来预测由PTB7-Th:PCBM混合物制成、以PFN作为电子传输层（ETL）、在氮气环境下制造的倒置有机太阳能电池（iOSC）的功率转换效率（PCE）。我们使用几种统计指标评估所提出技术的性能，包括均方误差（MSE）、均方根误差（rMSE）、相对平方误差（RSE）、相对绝对误差（RAE）和相关系数（R）。结果表明我们所提出的技术具有很高的准确性，预测的PCE值与实验测量值之间的误差极小，证明了这一点：使用LSTM模型时，RSE为0.0325，RAE为0.0729，rMSE为0.2223，MSE为 .0541。这些发现突出了所提出模型在准确预测有机光伏电池性能方面的潜力，从而有助于推动可持续能源技术的发展。
