keywords: Amino acids, DFT (B3LYP/6-31G*), QSAR, GFA, Molecular dynamics simulation
Experimental corrosion study was often high-cost and time-consuming since large-scale trial experiments were carried out. An In-silico method was used to study the inhibition performance of twenty-five amino acids and related compounds. Density Functional Theory (B3LYP/6-31G*) quantum chemical calculation method was used to find the optimized geometry of the studied inhibitors. Additionally, a linear quantitative structure-activity relationship (QSAR) model was built by Genetic Function Approximation (GFA) method to run the regression analysis and establish correlations between different types of descriptors and the measured corrosion inhibition efficiencies which was used to predict the corrosion inhibition efficiencies of the studied inhibitors. The prediction of corrosion efficiencies of these inhibitors nicely matched the experimental measurements. The correlation parameters obtained are R_train^2=0.98,R_adjusted^2=0.98,Q_LOO^2=0.97,〖 R〗_test^2=0.86. This indicates that the model was excellent on verifying with internal and external validation parameters. The affection of acidic solution was considered in molecular dynamics simulation and the calculated adsorption energies for most of the inhibitors is ˃100 kcal mol−1 suggesting chemisorptive interactions.
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