Citation
Abd Rahman, Mohd Amiruddin and Mohd Shazali, Nur Athirah and Anak Bundak, Caceja Elyca
(2025)
Pso-svr algorithm for accurate ZnO energy band gap prediction.
Materials Science Forum, 1144.
art. no. undefined.
pp. 19-28.
ISSN 0255-5476; eISSN: 1662-9752
Abstract
An artificial algorithm using a machine learning approach could be used to determine the energy band gap, Eg which would simply the process of synthesizing ZnO properties. This paper proposes to develop machine learning models that can accurately predict the energy band gap of ZnO. This study used PSO-SVR model utilizing three kernel functions: linear, polynomial, and RBF. The PSO-SVR with RBF resulted in the lowest RMSE of 0.0395eV. This analysis also showed that the combination of lattice constant a and c, crystallite size, D and grain size of ZnO datasets had contributed to high accuracy of predicting Eg.
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Official URL or Download Paper: https://www.scientific.net/MSF.1144.19
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Additional Metadata
| Item Type: | Article |
|---|---|
| Subject: | Materials Science (all) |
| Subject: | Condensed Matter Physics |
| Divisions: | Faculty of Science |
| DOI Number: | https://doi.org/10.4028/p-pQABg0 |
| Publisher: | Trans Tech Publications Ltd |
| Keywords: | Artificial intelligence; Energy band gap; Machine learning; Pso-svr |
| Sustainable Development Goals (SDGs): | SDG 9: Industry, Innovation and Infrastructure, SDG 7: Affordable and Clean Energy, SDG 12: Responsible Consumption and Production |
| Depositing User: | Ms. Siti Radziah Mohamed@mahmod |
| Date Deposited: | 22 Apr 2026 11:09 |
| Last Modified: | 22 Apr 2026 11:09 |
| Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.4028/p-pQABg0 |
| URI: | http://psasir.upm.edu.my/id/eprint/123481 |
| Statistic Details: | View Download Statistic |
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