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

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 (EL-H), the dipole moment (mu), the hardness (eta), the softness (sigma), the absolute electronegativity (chi), the ionization potential (IP), the electron affinity (EA), the fraction of electrons transferred (Delta N), the electrophilicity index omega 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-cv(2)= 0.92 and predicted determination coefficient R-pred(2) = 0.92 and R-pred(2) = 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-test(2) = 0.94 and R-test(2) = 0.92, for the two correspondingly models.