Introducing a Novel Figure of Merit for Evaluating Stability of Perovskite Solar Cells: Utilizing Long Short-Term Memory Neural Networks
This study introduces a novel figure of merit for evaluating the stability of perovskite solar cells (PSCs) by employing advanced Long Short-Term Memory (LSTM) neural networks to investigate degradation mechanisms. By harnessing the power of artificial intelligence and data analytics, we analyzed ex...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10924233/ |
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| author | Zahraa Ismail Ahmet Sait Alali Ahmad Muhammad Mahmoud Ashraf Sameh O. Abdellatif |
| author_facet | Zahraa Ismail Ahmet Sait Alali Ahmad Muhammad Mahmoud Ashraf Sameh O. Abdellatif |
| author_sort | Zahraa Ismail |
| collection | DOAJ |
| description | This study introduces a novel figure of merit for evaluating the stability of perovskite solar cells (PSCs) by employing advanced Long Short-Term Memory (LSTM) neural networks to investigate degradation mechanisms. By harnessing the power of artificial intelligence and data analytics, we analyzed extensive datasets encompassing PSC parameters, experimental results, and environmental conditions, revealing critical insights into the degradation patterns affecting cell performance over time. Our findings indicate that the LSTM model effectively captures and predicts the complex relationships between key design parameters—efficiency, fill factor, and open-circuit voltage—and degradation-induced changes in PSCs. Specifically, we identified three degradation coefficients associated with the electron transport layer, hole transport layer, and perovskite active layer. These coefficients serve as a new figure of merit, facilitating numerical studies on degradation and stability in PSCs, mainly focusing on cesium lead halides. Furthermore, the enhanced LSTM architecture, featuring deeper layers, dropout for regularization, and batch normalization, demonstrated improved stability and training speed, leading to a test Mean Absolute Error (MAE) of 0.0354 and an R2 value of 0.9991, indicating near-perfect predictive accuracy. The comparative analysis of model complexity confirmed that increasing the sophistication of the LSTM model significantly enhances predictive accuracy and generalization capabilities. Identifying crucial design parameters offers actionable insights for optimizing PSC designs, materials selection, and operational conditions, ultimately contributing to enhanced long-term stability and efficiency of PSCs. Future research should prioritize using experimental datasets to achieve more realistic predictions, thereby driving innovation and unlocking the full potential of machine learning and deep learning in optimizing PSC design and performance. |
| format | Article |
| id | doaj-art-4debdc729df14b0998ae3b6c75cf6bde |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-4debdc729df14b0998ae3b6c75cf6bde2025-08-20T02:54:26ZengIEEEIEEE Access2169-35362025-01-0113497354974910.1109/ACCESS.2025.355065810924233Introducing a Novel Figure of Merit for Evaluating Stability of Perovskite Solar Cells: Utilizing Long Short-Term Memory Neural NetworksZahraa Ismail0https://orcid.org/0000-0002-7720-5325Ahmet Sait Alali1https://orcid.org/0000-0002-7750-5571Ahmad Muhammad2https://orcid.org/0000-0003-3886-7956Mahmoud Ashraf3Sameh O. Abdellatif4https://orcid.org/0000-0001-8677-9497Electrical Engineering Department, FabLab, Centre for Emerging Learning Technologies (CELT), The British University in Egypt (BUE), Cairo, EgyptDepartment of Physics, Yıldız Technical University, Ïstanbul, TürkiyeDepartment of Physics and Materials Sciences, College of Arts and Sciences, Qatar University, Doha, QatarElectrical Engineering Department, FabLab, Centre for Emerging Learning Technologies (CELT), The British University in Egypt (BUE), Cairo, EgyptElectrical Engineering Department, FabLab, Centre for Emerging Learning Technologies (CELT), The British University in Egypt (BUE), Cairo, EgyptThis study introduces a novel figure of merit for evaluating the stability of perovskite solar cells (PSCs) by employing advanced Long Short-Term Memory (LSTM) neural networks to investigate degradation mechanisms. By harnessing the power of artificial intelligence and data analytics, we analyzed extensive datasets encompassing PSC parameters, experimental results, and environmental conditions, revealing critical insights into the degradation patterns affecting cell performance over time. Our findings indicate that the LSTM model effectively captures and predicts the complex relationships between key design parameters—efficiency, fill factor, and open-circuit voltage—and degradation-induced changes in PSCs. Specifically, we identified three degradation coefficients associated with the electron transport layer, hole transport layer, and perovskite active layer. These coefficients serve as a new figure of merit, facilitating numerical studies on degradation and stability in PSCs, mainly focusing on cesium lead halides. Furthermore, the enhanced LSTM architecture, featuring deeper layers, dropout for regularization, and batch normalization, demonstrated improved stability and training speed, leading to a test Mean Absolute Error (MAE) of 0.0354 and an R2 value of 0.9991, indicating near-perfect predictive accuracy. The comparative analysis of model complexity confirmed that increasing the sophistication of the LSTM model significantly enhances predictive accuracy and generalization capabilities. Identifying crucial design parameters offers actionable insights for optimizing PSC designs, materials selection, and operational conditions, ultimately contributing to enhanced long-term stability and efficiency of PSCs. Future research should prioritize using experimental datasets to achieve more realistic predictions, thereby driving innovation and unlocking the full potential of machine learning and deep learning in optimizing PSC design and performance.https://ieeexplore.ieee.org/document/10924233/Perovskite solar cellsdegradationfinite element modellong short-term memorypredictive modeling |
| spellingShingle | Zahraa Ismail Ahmet Sait Alali Ahmad Muhammad Mahmoud Ashraf Sameh O. Abdellatif Introducing a Novel Figure of Merit for Evaluating Stability of Perovskite Solar Cells: Utilizing Long Short-Term Memory Neural Networks IEEE Access Perovskite solar cells degradation finite element model long short-term memory predictive modeling |
| title | Introducing a Novel Figure of Merit for Evaluating Stability of Perovskite Solar Cells: Utilizing Long Short-Term Memory Neural Networks |
| title_full | Introducing a Novel Figure of Merit for Evaluating Stability of Perovskite Solar Cells: Utilizing Long Short-Term Memory Neural Networks |
| title_fullStr | Introducing a Novel Figure of Merit for Evaluating Stability of Perovskite Solar Cells: Utilizing Long Short-Term Memory Neural Networks |
| title_full_unstemmed | Introducing a Novel Figure of Merit for Evaluating Stability of Perovskite Solar Cells: Utilizing Long Short-Term Memory Neural Networks |
| title_short | Introducing a Novel Figure of Merit for Evaluating Stability of Perovskite Solar Cells: Utilizing Long Short-Term Memory Neural Networks |
| title_sort | introducing a novel figure of merit for evaluating stability of perovskite solar cells utilizing long short term memory neural networks |
| topic | Perovskite solar cells degradation finite element model long short-term memory predictive modeling |
| url | https://ieeexplore.ieee.org/document/10924233/ |
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