Using molecular dynamics simulations and genetic function approximation to model corrosion inhibition of iron in chloride solutions

Abstract

Density functional theory (DFT) calculations have been used to investigate the minimum energy structures of asparagine molecules on iron (Fe) (111) surface. Adsorption of the asparagine molecule on a Fe (111) surface has been studied computationally to generate adsorption configurations and to use the force field method to obtain a ranking of the energies for each generated configuration, thereby indicating the preferred adsorption sites. In this article Monte Carlo simulation has been used to find low energy adsorption sites on both adsorbate (asparagines)-substrate (Fe 111)-systems as the temperature of the system is gradually decreased. The results indicated that asparagine could adsorb on a Fe surface through the nitrogen/oxygen atoms with the lone pair of electrons in its molecule.The Quantitative Structure Activity Relationship (QSAR) method is becoming more desirable for predicting corrosion inhibition properties. The inhibition efficiency of organic compounds is dependent on many basic molecular descriptors, such as dipole moments, molecular surface area, molecular volume, electronic parameters as EHOMO (highest occupied molecular orbital energy); ELUMO (lowest unoccupied molecular orbital energy); and energy gap (ELUMO-EHOMO). A Genetic Function Approximation (GFA) method was used to run the regression analysis and establish correlations between different types of descriptors and the measured corrosion inhibition efficiencies of 28 amino acids and their related compounds. Similarly, a QSAR equation was developed and used to predict the corrosion inhibition efficiencies of 28 amino acids and their related compounds. The prediction of corrosion efficiencies of these compounds nicely matched the experimental measurements. © 2013 by ESG.