Cover Image

Determination of bandgap of period 3, 4, and 5 transition metal dopants on zinc oxide using an artificial neural network based approach

Artificial intelligence (AI) and machine learning (ML) have rapidly emerged as valuable tools for chemical research, offering new ways to analyze and understand complex chemical systems. This research article investigates the use of adaptive neuro-fuzzy inference system (ANFIS) and multi-layer perce...

Full description

Saved in:
Bibliographic Details
Main Authors: Saeed, Muhammad Haris, Abed Mohammed, Mazin, Kosar, Naveen, Hassan, Sadaf-ul, Nadeem, Sohail, Abd Ghani, Mohd Khanapi, Abdulkareem, Karrar Hameed
Format: Article
Language:English
Published: Elsevier B.V. 2023
Online Access:http://eprints.utem.edu.my/id/eprint/27752/1/0071712022024234733.pdf
http://eprints.utem.edu.my/id/eprint/27752/
https://www.sciencedirect.com/science/article/pii/S0169743923002332
https://doi.org/10.1016/j.chemolab.2023.104983
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utem.eprints.27752
record_format eprints
spelling my.utem.eprints.277522024-10-07T12:35:08Z http://eprints.utem.edu.my/id/eprint/27752/ Determination of bandgap of period 3, 4, and 5 transition metal dopants on zinc oxide using an artificial neural network based approach Saeed, Muhammad Haris Abed Mohammed, Mazin Kosar, Naveen Hassan, Sadaf-ul Nadeem, Sohail Abd Ghani, Mohd Khanapi Abdulkareem, Karrar Hameed Artificial intelligence (AI) and machine learning (ML) have rapidly emerged as valuable tools for chemical research, offering new ways to analyze and understand complex chemical systems. This research article investigates the use of adaptive neuro-fuzzy inference system (ANFIS) and multi-layer perceptron (MLP) models to predict the bandgap of transition metal doped zinc oxide (ZnO). The opto-electronic properties of transition metal doped ZnO complexes are of significant interest because of their applications is optoelectronic systems. The MLP and ANFIS models were trained using a dataset of experimentally measured bandgap values and the corresponding structural parameters of the doped ZnO systems. The performance of the models was evaluated using statistical metrics i.e., RMSE, R, and MAE. The results showed that both MLP and ANFIS models were capable of accurately predicting the bandgap of transition metal doped ZnO. However, the ANFIS model demonstrated superior performance with higher accuracy and better generalization ability. The study provides a useful approach for predicting the bandgap of transition metal doped ZnO using machine learning techniques and may contribute to the development of advanced optoelectronic devices. Elsevier B.V. 2023 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/27752/1/0071712022024234733.pdf Saeed, Muhammad Haris and Abed Mohammed, Mazin and Kosar, Naveen and Hassan, Sadaf-ul and Nadeem, Sohail and Abd Ghani, Mohd Khanapi and Abdulkareem, Karrar Hameed (2023) Determination of bandgap of period 3, 4, and 5 transition metal dopants on zinc oxide using an artificial neural network based approach. Chemometrics and Intelligent Laboratory Systems, 242 (104983). pp. 1-11. ISSN 0169-7439 https://www.sciencedirect.com/science/article/pii/S0169743923002332 https://doi.org/10.1016/j.chemolab.2023.104983
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Artificial intelligence (AI) and machine learning (ML) have rapidly emerged as valuable tools for chemical research, offering new ways to analyze and understand complex chemical systems. This research article investigates the use of adaptive neuro-fuzzy inference system (ANFIS) and multi-layer perceptron (MLP) models to predict the bandgap of transition metal doped zinc oxide (ZnO). The opto-electronic properties of transition metal doped ZnO complexes are of significant interest because of their applications is optoelectronic systems. The MLP and ANFIS models were trained using a dataset of experimentally measured bandgap values and the corresponding structural parameters of the doped ZnO systems. The performance of the models was evaluated using statistical metrics i.e., RMSE, R, and MAE. The results showed that both MLP and ANFIS models were capable of accurately predicting the bandgap of transition metal doped ZnO. However, the ANFIS model demonstrated superior performance with higher accuracy and better generalization ability. The study provides a useful approach for predicting the bandgap of transition metal doped ZnO using machine learning techniques and may contribute to the development of advanced optoelectronic devices.
format Article
author Saeed, Muhammad Haris
Abed Mohammed, Mazin
Kosar, Naveen
Hassan, Sadaf-ul
Nadeem, Sohail
Abd Ghani, Mohd Khanapi
Abdulkareem, Karrar Hameed
spellingShingle Saeed, Muhammad Haris
Abed Mohammed, Mazin
Kosar, Naveen
Hassan, Sadaf-ul
Nadeem, Sohail
Abd Ghani, Mohd Khanapi
Abdulkareem, Karrar Hameed
Determination of bandgap of period 3, 4, and 5 transition metal dopants on zinc oxide using an artificial neural network based approach
author_facet Saeed, Muhammad Haris
Abed Mohammed, Mazin
Kosar, Naveen
Hassan, Sadaf-ul
Nadeem, Sohail
Abd Ghani, Mohd Khanapi
Abdulkareem, Karrar Hameed
author_sort Saeed, Muhammad Haris
title Determination of bandgap of period 3, 4, and 5 transition metal dopants on zinc oxide using an artificial neural network based approach
title_short Determination of bandgap of period 3, 4, and 5 transition metal dopants on zinc oxide using an artificial neural network based approach
title_full Determination of bandgap of period 3, 4, and 5 transition metal dopants on zinc oxide using an artificial neural network based approach
title_fullStr Determination of bandgap of period 3, 4, and 5 transition metal dopants on zinc oxide using an artificial neural network based approach
title_full_unstemmed Determination of bandgap of period 3, 4, and 5 transition metal dopants on zinc oxide using an artificial neural network based approach
title_sort determination of bandgap of period 3, 4, and 5 transition metal dopants on zinc oxide using an artificial neural network based approach
publisher Elsevier B.V.
publishDate 2023
url http://eprints.utem.edu.my/id/eprint/27752/1/0071712022024234733.pdf
http://eprints.utem.edu.my/id/eprint/27752/
https://www.sciencedirect.com/science/article/pii/S0169743923002332
https://doi.org/10.1016/j.chemolab.2023.104983
_version_ 1814061423051931648
score 13.211869

Similar Items