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

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.

Investigasi Model Machine Learning Berbasis QSPR pada Inhibitor Korosi Pirimidin

“…Deskriptor kimia kuantum hasil perhitungan teori fungsional kerapatan/density functional theory (DFT) memainkan peran penting dalam konstruksi model QSPR yang dapat diandalkan dan tepat. Secara umum, seleksi fitur dilakukan untuk mendapatkan deskriptor kimia kuantum yang relevan yang akan digunakan dalam pembangunan model QSPR (El Assiri et al, 2020;Quadri et al, 2022b). Pemodelan QSPR dari berbagai deskriptor kimia kuantum menggunakan regresi linier dan non-linier telah mendapat banyak perhatian.…”

“…Elektronegativitas mengacu pada kapasitas molekul inhibitor untuk menarik elektron dan mencapai kesetimbangan elektron. Global hardness mewakili resistensi molekul terhadap transfer muatan, sedangkan global softness menunjukkan kemampuan molekul untuk menerima muatan (Camacho-Mendoza et al, 2022;El Assiri et al, 2020;Li et al, 2015;Ser et al, 2020). Momen dipol molekul menggambarkan kemampuan molekul untuk berinteraksi dengan dipol permukaan logam.…”

Investigation of Best QSPR-Based Machine Learning Model to Predict Corrosion Inhibition Performance of Pyridine-Quinoline Compounds

“…The ability of the inhibitor molecule to attract electrons and achieve electron equilibrium is referred to as electronegativity. Global hardness indicates the capacity of a molecule to accept a charge, while global softness indicates a molecule's resistance to charge transfer [13], [14], [17]- [19]. The ability of a molecule to interact with a metal surface dipole is described by the molecular dipole moment.…”

Perbandingan Algoritma Multilinear Regression dan Decision Tree Regressor dalam Memprediksi Efisiensi Penghambatan Korosi Piridazin

“…Untuk mengatasi permasalahan tesebut, pendekatan Machine Learning (ML) menjadi solusi efektif dalam evaluasi senyawa inhibitor korosi. ML dapat digunakan untuk mengevaluasi kinerja penghambatan korosi suatu senyawa karena adanya hubungan kuantitatif antara struktur senyawa dengan sifat-sifat maupun aktivitas molekulernya (El Assiri et al, 2020;Quadri et al, 2022;Sutojo et al, 2023). Quadri et al (Quadri et al, 2022) membandingkan model artificial neural network (ANN) dan multilinear regression (MLR) pada senyawa piridazin, hasilnya menunjukkan bahwa model ANN lebih optimal dengan nilai MSE, RMSE, dan MAPE masing-masing adalah 111.5910, 10.5637, dan 10.2362.…”