Development and validation of QSPR models for corrosion inhibition of carbon steel by some pyridazine derivatives in acidic medium

El Hassan El Assiri; Majid Driouch; Jamila Lazrak; Zakariae Bensouda; Ali Elhaloui; Mouhcine Sfaira; Taoufiq Saffaj; Mustapha Taleb

doi:10.1016/j.heliyon.2020.e05067

Development and validation of QSPR models for corrosion inhibition of carbon steel by some pyridazine derivatives in acidic medium

Heliyon. 2020 Oct 5;6(10):e05067. doi: 10.1016/j.heliyon.2020.e05067. eCollection 2020 Oct.

Authors

El Hassan El Assiri¹, Majid Driouch¹, Jamila Lazrak², Zakariae Bensouda¹, Ali Elhaloui^{1

3}, Mouhcine Sfaira¹, Taoufiq Saffaj⁴, Mustapha Taleb²

Affiliations

¹ Laboratory of Engineering, Modeling and Systems Analysis, LIMAS, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, USMBA, Po. Box 1796, Atlas Fez, Morocco.
² Laboratory of Engineering, Electrochemistry, Modeling and Environment, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, USMBA, Po. Box 1796 Atlas Fez, Morocco.
³ Laboratory of Materials, Electrochemistry and Environment, Faculty of Sciences, Ibn Tofaîl University, Po. Box 133-14000 Kénitra, Morocco.
⁴ Laboratory of Application Organic Chemistry, Faculty of Sciences and Techniques, Sidi Mohamed Ben Abdellah University, USMBA, Po. Box 2626 Fez, Morocco.

Abstract

Statistical modeling of the corrosion inhibition process by twenty-one pyridazine derivatives for mild steel in acidic medium was investigated by the quantitative structure property relationship (QSPR) approach. This modeling was established by the correlation between the corrosion inhibition efficiency (IE %) and a number of the electronic and structural properties of these inhibitors such as: the E _HOMO (highest occupied molecular orbital energy), the E _LUMO (lowest unoccupied molecular orbital energy), the energy gap (E _L-H ), the dipole moment (μ), the hardness (η), the softness (σ), the absolute electronegativity (χ), the ionization potential (IP), the electron affinity (EA), the fraction of electrons transferred (ΔN), the electrophilicity index ω the molecular volume (V _m ), the logarithm of the partition coefficient (Log P), and the molecular mass (M), in addition to the inhibitor concentration (C _i ). The structure electronic properties was calculated by the use of the density functional theory method (DFT), at B3LYP/6-31G (d, p) level of theory and the analysis of dimensionality and redundancy as well as the test of collinearity between descriptors are carried out using principal component analysis (PCA). Whereas, the correlation between EI % and molecular structure is performed through the development of tree mathematical models, based-QSPR approaches: the partial least squares regression (PLS), the principal component regression (PCR) and the artificial neural networks (ANN). Indeed, the statistical quantitative results revealed that PCR and ANN were the most relevant and predictive models in comparison with the PLS model. This pertinence was demonstrated by using leave one-out cross-validation as an efficient method for testing the internal stability and predictive capability of said models with a high cross-validated determination coefficient R ² _cv = 0.92 and predicted determination coefficient R ² _pred = 0.92 and R ² _pred = 0.90 for PCR and ANN respectively; in addition to an extrapolation test set as an external validation with a significant external coefficient of determination: R ² _test = 0.94 and R ² _test = 0.92, for the two correspondingly models.

Keywords: Artificial neural networks; Corrosion inhibition; Cross validation; DFT calculations; Materials chemistry; Partial least squares regression; Principal component regression; Principal components analysis; Theoretical chemistry.