Cover Image

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Aidil Zulkafli, Nadhirah, Elyca Anak Bundak, Caceja, Amiruddin Abd Rahman, Mohd, Chin Yap, Chi, Chong, Kok-Keong, Tee Tan, Sin
Format: Article
Language:English
Published: Elsevier 2024
Online Access:http://psasir.upm.edu.my/id/eprint/113626/1/113626.pdf
http://psasir.upm.edu.my/id/eprint/113626/
https://linkinghub.elsevier.com/retrieve/pii/S0038092X24004900
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.113626
record_format eprints
spelling my.upm.eprints.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
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
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.
format Article
author Aidil Zulkafli, Nadhirah
Elyca Anak Bundak, Caceja
Amiruddin Abd Rahman, Mohd
Chin Yap, Chi
Chong, Kok-Keong
Tee Tan, Sin
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
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
title 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_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_sort prediction of device performance in sno2 based inverted organic solar cells using machine learning framework
publisher Elsevier
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/113626/1/113626.pdf
http://psasir.upm.edu.my/id/eprint/113626/
https://linkinghub.elsevier.com/retrieve/pii/S0038092X24004900
_version_ 1816132753579573248
score 13.223943

Similar Items