Modelling the optical band Gap energy of undoped ZnO Thin films by supervised machine learning methods
Author
Source
African Journal of Advanced Pure and Applied Sciences
Issue
Vol. 2, Issue 2 (30 Apr. 2023), pp.137-147, 11 p.
Publisher
African Academy of Advanced Studies
Publication Date
2023-04-30
Country of Publication
Libya
No. of Pages
11
Main Subjects
Abstract EN
Recently, machine learning (ML) methods are growing as one of the most powerful techniques in scientific research and technological applications.
herein, artificial neural networks (ANNs) as novel predictive ML techniques were built to predict the optical band gap energy of undoped ZnO thin films.
the proposed multilayer perceptron neural network methods include the scaled conjugate gradient (SCG) and the gradient descent (GD) with momentum and learning rate optimization coefficients.
the two suggested techniques were trained, tested, and validated with empirical data sets, by selecting the temperature of the substrate and the precursor molarity of ZnO solution as input parameters as well as the band gap energy as a response parameter.
furthermore, the simulated findings of ANN models were compared to the multiple linear regression (MLR) model and then the fitness and accuracy of those models were evaluated by different statistical metrics including the root mean square error (RMSE), the mean absolute percentage error (MAPE), and regression coefficients.
based on the results, SCG-ANN and GD-ANN models show high prediction accuracy with RMSE of 0.055 and 0.064 for testing data, respectively, whilst MLR analysis showed poor prediction accuracy with RMSE of 0.080 and R2 of 0.063.
additionally, the simulated output of these proposed ANNs models is in good agreement with the empirical datasets, which indicates high performance of SCG-ANN and GD-ANN models than the MLR model.
American Psychological Association (APA)
Akhwatir, Maryam. 2023. Modelling the optical band Gap energy of undoped ZnO Thin films by supervised machine learning methods. African Journal of Advanced Pure and Applied Sciences،Vol. 2, no. 2, pp.137-147.
https://search.emarefa.net/detail/BIM-1506825
Modern Language Association (MLA)
Akhwatir, Maryam. Modelling the optical band Gap energy of undoped ZnO Thin films by supervised machine learning methods. African Journal of Advanced Pure and Applied Sciences Vol. 2, no. 2 (Apr. / Jun. 2023), pp.137-147.
https://search.emarefa.net/detail/BIM-1506825
American Medical Association (AMA)
Akhwatir, Maryam. Modelling the optical band Gap energy of undoped ZnO Thin films by supervised machine learning methods. African Journal of Advanced Pure and Applied Sciences. 2023. Vol. 2, no. 2, pp.137-147.
https://search.emarefa.net/detail/BIM-1506825
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 146-147
Record ID
BIM-1506825