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HomeMaterials Science ForumMaterials Science Forum Vol. 1144PSO-SVR Algorithm for Accurate ZnO Energy Band Gap...

PSO-SVR Algorithm for Accurate ZnO Energy Band Gap Prediction

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Abstract:

An artificial algorithm using a machine learning approach could be used to determine the energy band gap, E_g 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 E_g.

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Materials Science Forum (Volume 1144)

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19-28

DOI:

https://doi.org/10.4028/p-pQABg0

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Online since:

March 2025

Authors:

Mohd Amiruddin Abd Rahman*, Nur Athirah Mohd Shazali, Caceja Elyca Anak Bundak

Keywords:

Artificial Intelligence, Energy Band Gap, Machine Learning, PSO-SVR

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© 2025 Trans Tech Publications Ltd. All Rights Reserved

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