Machine learning predictor models in the electronic properties of alkanes based on degree-topology indices

New topology indices that are degree-based have been introduced to represent molecular structure from chemical graph theory. The indices give a new sight into the physical properties of the chemical compounds. The correlation of physiochemical properties with chemical graph theory can be done using...

Full description

Bibliographic Details
Published in:International Journal of Emerging Technology and Advanced Engineering
Main Author: Zabidi Z.M.; Alias A.N.; Zakaria N.A.; Mahmud Z.S.; Ali R.; Yaakob M.K.; Masrom S.
Format: Article
Language:English
Published: IJETAE Publication House 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120468873&doi=10.46338%2fIJETAE1121_01&partnerID=40&md5=d8caa95f1d4adaa544a9f6954f24f710
id 2-s2.0-85120468873
spelling 2-s2.0-85120468873
Zabidi Z.M.; Alias A.N.; Zakaria N.A.; Mahmud Z.S.; Ali R.; Yaakob M.K.; Masrom S.
Machine learning predictor models in the electronic properties of alkanes based on degree-topology indices
2021
International Journal of Emerging Technology and Advanced Engineering
11
11
10.46338/IJETAE1121_01
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120468873&doi=10.46338%2fIJETAE1121_01&partnerID=40&md5=d8caa95f1d4adaa544a9f6954f24f710
New topology indices that are degree-based have been introduced to represent molecular structure from chemical graph theory. The indices give a new sight into the physical properties of the chemical compounds. The correlation of physiochemical properties with chemical graph theory can be done using the Quantitative Structure Properties Relationship (QSPR). Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) are two basic electronic properties that describe the physiochemical of molecular structure. In computational chemistry, HOMO and LUMO can be calculated by ab initio molecular orbital calculation such as semi-empirical and density functional theory (DFT) method. However, these methods are time-consuming computations. In this paper, predictor model of HOMO and LUMO were developed using Machine Learning algorithms namely Linear Regression, Ridge Regression, LASSO Regression and Elastic Net Regression. The results showed that the performance achievement of each of the machine learning algorithms varied in accordance to the topology indices descriptors and the most outperformed model was presented by Linear Regression with the Moment Balaban Indices (JJ). This paper provides the fundamental design and implementation framework of predicting the HOMO and LUMO electronic properties. © 2021 IJETAE Publication House. All Rights Reserved.
IJETAE Publication House
22502459
English
Article
All Open Access; Bronze Open Access
author Zabidi Z.M.; Alias A.N.; Zakaria N.A.; Mahmud Z.S.; Ali R.; Yaakob M.K.; Masrom S.
spellingShingle Zabidi Z.M.; Alias A.N.; Zakaria N.A.; Mahmud Z.S.; Ali R.; Yaakob M.K.; Masrom S.
Machine learning predictor models in the electronic properties of alkanes based on degree-topology indices
author_facet Zabidi Z.M.; Alias A.N.; Zakaria N.A.; Mahmud Z.S.; Ali R.; Yaakob M.K.; Masrom S.
author_sort Zabidi Z.M.; Alias A.N.; Zakaria N.A.; Mahmud Z.S.; Ali R.; Yaakob M.K.; Masrom S.
title Machine learning predictor models in the electronic properties of alkanes based on degree-topology indices
title_short Machine learning predictor models in the electronic properties of alkanes based on degree-topology indices
title_full Machine learning predictor models in the electronic properties of alkanes based on degree-topology indices
title_fullStr Machine learning predictor models in the electronic properties of alkanes based on degree-topology indices
title_full_unstemmed Machine learning predictor models in the electronic properties of alkanes based on degree-topology indices
title_sort Machine learning predictor models in the electronic properties of alkanes based on degree-topology indices
publishDate 2021
container_title International Journal of Emerging Technology and Advanced Engineering
container_volume 11
container_issue 11
doi_str_mv 10.46338/IJETAE1121_01
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120468873&doi=10.46338%2fIJETAE1121_01&partnerID=40&md5=d8caa95f1d4adaa544a9f6954f24f710
description New topology indices that are degree-based have been introduced to represent molecular structure from chemical graph theory. The indices give a new sight into the physical properties of the chemical compounds. The correlation of physiochemical properties with chemical graph theory can be done using the Quantitative Structure Properties Relationship (QSPR). Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) are two basic electronic properties that describe the physiochemical of molecular structure. In computational chemistry, HOMO and LUMO can be calculated by ab initio molecular orbital calculation such as semi-empirical and density functional theory (DFT) method. However, these methods are time-consuming computations. In this paper, predictor model of HOMO and LUMO were developed using Machine Learning algorithms namely Linear Regression, Ridge Regression, LASSO Regression and Elastic Net Regression. The results showed that the performance achievement of each of the machine learning algorithms varied in accordance to the topology indices descriptors and the most outperformed model was presented by Linear Regression with the Moment Balaban Indices (JJ). This paper provides the fundamental design and implementation framework of predicting the HOMO and LUMO electronic properties. © 2021 IJETAE Publication House. All Rights Reserved.
publisher IJETAE Publication House
issn 22502459
language English
format Article
accesstype All Open Access; Bronze Open Access
record_format scopus
collection Scopus
_version_ 1809678481610506240