New QSPR model for prediction of corrosion inhibition using conceptual density functional theory

• Published in: Journal of molecular modeling Vol. 28; no. 8; p. 238
• Main Authors: Camacho-Mendoza, Rosa L., Feria, Leticia, Zárate-Hernández, Luis Ángel, Alvarado-Rodríguez, José G., Cruz-Borbolla, Julián
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2022; Springer Nature B.V
• Subjects: Approximation; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Computer Appl. in Life Sciences; Computer Applications in Chemistry; Corrosion inhibitors; Density functional theory; Genetic algorithms; Iron; Mathematical analysis; Molecular Medicine; Original Paper; Quinoline; Regression analysis; Theoretical and Computational Chemistry; QSPR; DFT; Hardness; Heterocyclic compounds; Corrosion inhibitor
• ISSN: 1610-2940; 0948-5023
• DOI: 10.1007/s00894-022-05240-6

The relationship between structure and corrosion inhibition of a series of twenty-eight quinoline and pyridine derivatives has been established through the investigation of quantum descriptors calculated with PBE/6–311 +  + G** method. A quantitative structure–property relationship (QSPR) model was obtained by examining these descriptors using a genetic algorithm approximation method based on a multiple linear regression analysis. The results indicate that the efficiency of corrosion inhibitors is strongly associated with hardness ( η ), minimal electrostatic potential (ESP min ), and volume ( V ) descriptors. Furthermore, the validity of the proposed model is corroborated by an adsorption study on an iron surface Fe(110).