Prediction of device performance in SnO2 based inverted organic solar cells using machine learning framework
The development of wearable electronic gadgets has spanned the research attention toward the design of flexible and high-performance organic solar cells. The complicated process and long data execution time have limited its research progress. In this project, the machine learning (ML) framework with...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/113626/ http://psasir.upm.edu.my/id/eprint/113626/1/113626.pdf |
| _version_ | 1848866279307345920 |
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| author | Aidil Zulkafli, Nadhirah Elyca Anak Bundak, Caceja Amiruddin Abd Rahman, Mohd Chin Yap, Chi Chong, Kok-Keong Tee Tan, Sin |
| author_facet | Aidil Zulkafli, Nadhirah Elyca Anak Bundak, Caceja Amiruddin Abd Rahman, Mohd Chin Yap, Chi Chong, Kok-Keong Tee Tan, Sin |
| author_sort | Aidil Zulkafli, Nadhirah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The development of wearable electronic gadgets has spanned the research attention toward the design of flexible and high-performance organic solar cells. The complicated process and long data execution time have limited its research progress. In this project, the machine learning (ML) framework with different algorithm models and kernel functions was employed to predict the device performance of solution-processed SnO2-based organic solar cells. The device performance of the SnO2 prepared using different spinning rates was used as the training data for machine learning prediction. The accuracy of the prediction was controlled using the root-mean-square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The comparison between the measured and predicted value of the device parameters such as open circuit voltage (Voc), short circuit current density (Jsc), fill factor (FF), and power conversion efficiency (PCE) was discussed. The radial basis support vector regression (SVR) integrated with particle swarm optimization (PSO) model showed the highest performance in predicting the PCE of SnO2-based organic solar cells with R2 of 99%, RMSE of 0.0119 and MAPE of 0.0075. This novel study demonstrated that support vector regression (SVR) integrated with the particle swarm optimization (PSO) model is an alternative method to predict the device performance in future organic solar cells. |
| first_indexed | 2025-11-15T14:18:04Z |
| format | Article |
| id | upm-113626 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:18:04Z |
| publishDate | 2024 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1136262024-11-14T03:09:59Z http://psasir.upm.edu.my/id/eprint/113626/ Prediction of device performance in SnO2 based inverted organic solar cells using machine learning framework Aidil Zulkafli, Nadhirah Elyca Anak Bundak, Caceja Amiruddin Abd Rahman, Mohd Chin Yap, Chi Chong, Kok-Keong Tee Tan, Sin The development of wearable electronic gadgets has spanned the research attention toward the design of flexible and high-performance organic solar cells. The complicated process and long data execution time have limited its research progress. In this project, the machine learning (ML) framework with different algorithm models and kernel functions was employed to predict the device performance of solution-processed SnO2-based organic solar cells. The device performance of the SnO2 prepared using different spinning rates was used as the training data for machine learning prediction. The accuracy of the prediction was controlled using the root-mean-square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The comparison between the measured and predicted value of the device parameters such as open circuit voltage (Voc), short circuit current density (Jsc), fill factor (FF), and power conversion efficiency (PCE) was discussed. The radial basis support vector regression (SVR) integrated with particle swarm optimization (PSO) model showed the highest performance in predicting the PCE of SnO2-based organic solar cells with R2 of 99%, RMSE of 0.0119 and MAPE of 0.0075. This novel study demonstrated that support vector regression (SVR) integrated with the particle swarm optimization (PSO) model is an alternative method to predict the device performance in future organic solar cells. Elsevier 2024 Article PeerReviewed text en cc_by_nc_4 http://psasir.upm.edu.my/id/eprint/113626/1/113626.pdf Aidil Zulkafli, Nadhirah and Elyca Anak Bundak, Caceja and Amiruddin Abd Rahman, Mohd and Chin Yap, Chi and Chong, Kok-Keong and Tee Tan, Sin (2024) Prediction of device performance in SnO2 based inverted organic solar cells using machine learning framework. Solar Energy, 278. art. no. 112795. pp. 1-9. ISSN 0038-092X https://linkinghub.elsevier.com/retrieve/pii/S0038092X24004900 10.1016/j.solener.2024.112795 |
| spellingShingle | Aidil Zulkafli, Nadhirah Elyca Anak Bundak, Caceja Amiruddin Abd Rahman, Mohd Chin Yap, Chi Chong, Kok-Keong Tee Tan, Sin Prediction of device performance in SnO2 based inverted organic solar cells using machine learning framework |
| title | Prediction of device performance in SnO2 based inverted organic solar cells using machine learning framework |
| title_full | Prediction of device performance in SnO2 based inverted organic solar cells using machine learning framework |
| title_fullStr | Prediction of device performance in SnO2 based inverted organic solar cells using machine learning framework |
| title_full_unstemmed | Prediction of device performance in SnO2 based inverted organic solar cells using machine learning framework |
| title_short | Prediction of device performance in SnO2 based inverted organic solar cells using machine learning framework |
| title_sort | prediction of device performance in sno2 based inverted organic solar cells using machine learning framework |
| url | http://psasir.upm.edu.my/id/eprint/113626/ http://psasir.upm.edu.my/id/eprint/113626/ http://psasir.upm.edu.my/id/eprint/113626/ http://psasir.upm.edu.my/id/eprint/113626/1/113626.pdf |