Multilayer perceptron neural network-based QSAR models for the assessment and prediction of corrosion inhibition performances of ionic liquids

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dc.authoridEBENSO, ENO/0000-0002-0411-9258
dc.authoridLee, Han-Seung/0000-0001-9776-5859
dc.contributor.authorQuadri, Taiwo W.
dc.contributor.authorOlasunkanmi, Lukman O.
dc.contributor.authorFayemi, Omolola E.
dc.contributor.authorAkpan, Ekemini D.
dc.contributor.authorLee, Han Seung
dc.contributor.authorLgaz, Hassane
dc.contributor.authorVerma, Chandrabhan
dc.date.accessioned2024-10-26T18:11:32Z
dc.date.available2024-10-26T18:11:32Z
dc.date.issued2022
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractThe present study reports the quantum chemical studies and quantitative structure activity relationship (QSAR) modeling of thirty ionic liquids utilized as chemical additives to repress mild steel degradation in 1.0 M HCl. Five molecular descriptors obtained from standardization of calculated descriptors together with the inhibitor con-centration were employed in model building. Multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN) modeling were utilized in model construction. The optimal MLPNN model was developed using a network architecture of 6-3-5-1 with Levenberg-Marquardt as the learning algorithm. The model yielded an MSE of 29.9242, RMSE of 5.4703, MAD of 4.9628, MAPE of 5.7809, rMBE of 0.1202 and CoV of 0.0052. The MLPNN model displayed better predictive performance than the MLR model. Furthermore, developed models were applied to forecast the inhibition efficiencies of five novel ionic liquids. The theoretical inhibitors were found to be effective inhibitors of steel corrosion, showing over 80% inhibition efficiency.
dc.description.sponsorshipNational Research Foundation of Korea (NRF) - Korean government (MSIT) [NRF-2018R1A5A1025137]
dc.description.sponsorshipThe authors are grateful to the Centre for High Performance Computing (CHPC) , CSIR, South Africa for the access granted to computational resources for the study. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2018R1A5A1025137) .
dc.identifier.doi10.1016/j.commatsci.2022.111753
dc.identifier.issn0927-0256
dc.identifier.issn1879-0801
dc.identifier.scopus2-s2.0-85136537775
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.commatsci.2022.111753
dc.identifier.urihttps://hdl.handle.net/20.500.12418/30720
dc.identifier.volume214
dc.identifier.wosWOS:000860431200002
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofComputational Materials Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCorrosion inhibition
dc.subjectIonic liquids
dc.subjectQSAR
dc.subjectMLR model
dc.subjectMLPNN model
dc.titleMultilayer perceptron neural network-based QSAR models for the assessment and prediction of corrosion inhibition performances of ionic liquids
dc.typeArticle

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