Predicting protection capacities of pyrimidine-based corrosion inhibitors for mild steel/HCl interface using linear and nonlinear QSPR models

• Published in: Journal of molecular modeling Vol. 28; no. 9; p. 254
• Main Authors: Quadri, Taiwo W., Olasunkanmi, Lukman O., Fayemi, Omolola E., Lgaz, Hassane, Dagdag, Omar, Sherif, El-Sayed M., Akpan, Ekemini D., Lee, Han-Seung, Ebenso, Eno E.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-09-2022; Springer Nature B.V
• Subjects: Additives; Algorithms; Artificial neural networks; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Computer Appl. in Life Sciences; Computer Applications in Chemistry; Corrosion; Corrosion inhibitors; Corrosion mechanisms; Corrosion prevention; Low carbon steels; Molecular Medicine; Multilayer perceptrons; Neural networks; Organic compounds; Original Paper; Prediction models; Regression analysis; Regression models; Substrates; Theoretical and Computational Chemistry; MLR model; Pyrimidines; QSPR; ANN model; Corrosion inhibitors; Chemical descriptors
• ISSN: 1610-2940; 0948-5023
• DOI: 10.1007/s00894-022-05245-1

Pyrimidine compounds have proven to be effective and efficient additives capable of protecting mild steel in acidic media. This class of organic compounds often functions as adsorption-type inhibitors of corrosion by forming a protective layer on the metallic substrate. The present study reports a computational study of forty pyrimidine compounds that have been investigated as sustainable inhibitors of mild steel corrosion in molar HCl solution. Quantitative structure property relationship was conducted using linear (multiple linear regression) and nonlinear (artificial neural network) models. Standardization method was employed in variable selection yielding five top chemical descriptors utilized for model development along with the inhibitor concentration. Multiple linear regression model yielded a fair predictive model. Artificial neural network model developed using k-fold cross-validation method provided a comprehensive insight into the corrosion protection mechanism of studied pyrimidine-based corrosion inhibitors. Using a multilayer perceptron with Levenberg–Marquardt algorithm, the study obtained the optimal model having a MSE of 8.479, RMSE of 2.912, MAD of 1.791, and MAPE of 2.648. The optimal neural network model was further utilized to forecast the protection capacities of nine non-synthesized pyrimidine derivatives. The predicted inhibition efficiencies ranged from 89 to 98%, revealing the significance of the considered chemical descriptors, the predictive capacity of the developed model, and the potency of the theoretical inhibitors.