Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach
Summary: Remarkable and intelligent perovskite solar cells (PSCs) have attracted substantial attention from researchers and are undergoing rapid advancements in photovoltaic technology. These developments aim to create highly efficient energy devices with fewer dominant recombination losses within t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004224004218 |
| _version_ | 1797299680622673920 |
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| author | Basir Akbar Hilal Tayara Kil To Chong |
| author_facet | Basir Akbar Hilal Tayara Kil To Chong |
| author_sort | Basir Akbar |
| collection | DOAJ |
| description | Summary: Remarkable and intelligent perovskite solar cells (PSCs) have attracted substantial attention from researchers and are undergoing rapid advancements in photovoltaic technology. These developments aim to create highly efficient energy devices with fewer dominant recombination losses within the realm of third-generation solar cells. Diverse machine learning (ML) algorithms implemented, addressing dominant losses due to recombination in PSCs, focusing on grain boundaries (GBs), interfaces, and band-to-band recombination. The extreme gradient boosting (XGBoost) classifier effectively predicts the recombination losses. Our model trained with 7-fold cross-validation to ensure generalizability and robustness. Leveraging Optuna and shapley additive explanations (SHAP) for hyperparameter optimization and investigate the influence of features on target variables, achieved 85% accuracy on over 2 million simulated data, respectively. Because of the input parameters (light intensity and open-circuit voltage), the performance evaluation measures for the dominant losses caused by the recombination predicted by proposed model were superior to those of state-of-the-art models. |
| first_indexed | 2024-03-07T22:54:11Z |
| format | Article |
| id | doaj.art-4c3bcf41448744ec823144b30ceb2345 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-07T22:54:11Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-4c3bcf41448744ec823144b30ceb23452024-02-23T05:00:34ZengElsevieriScience2589-00422024-03-01273109200Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approachBasir Akbar0Hilal Tayara1Kil To Chong2Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, South KoreaSchool of International Engineering and Science, Jeonbuk National University, Jeonju 54896, South Korea; Corresponding authorAdvances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea; Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea; Corresponding authorSummary: Remarkable and intelligent perovskite solar cells (PSCs) have attracted substantial attention from researchers and are undergoing rapid advancements in photovoltaic technology. These developments aim to create highly efficient energy devices with fewer dominant recombination losses within the realm of third-generation solar cells. Diverse machine learning (ML) algorithms implemented, addressing dominant losses due to recombination in PSCs, focusing on grain boundaries (GBs), interfaces, and band-to-band recombination. The extreme gradient boosting (XGBoost) classifier effectively predicts the recombination losses. Our model trained with 7-fold cross-validation to ensure generalizability and robustness. Leveraging Optuna and shapley additive explanations (SHAP) for hyperparameter optimization and investigate the influence of features on target variables, achieved 85% accuracy on over 2 million simulated data, respectively. Because of the input parameters (light intensity and open-circuit voltage), the performance evaluation measures for the dominant losses caused by the recombination predicted by proposed model were superior to those of state-of-the-art models.http://www.sciencedirect.com/science/article/pii/S2589004224004218Machine learningElectronic engineeringEnergy application |
| spellingShingle | Basir Akbar Hilal Tayara Kil To Chong Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach iScience Machine learning Electronic engineering Energy application |
| title | Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach |
| title_full | Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach |
| title_fullStr | Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach |
| title_full_unstemmed | Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach |
| title_short | Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach |
| title_sort | unveiling dominant recombination loss in perovskite solar cells with a xgboost based machine learning approach |
| topic | Machine learning Electronic engineering Energy application |
| url | http://www.sciencedirect.com/science/article/pii/S2589004224004218 |
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