Computational Prediction of Electrochemical Corrosion Rates of Copper in the Presence of Corrosion Inhibitors

Abstract

Computational prediction of corrosion rates is still a challenging issue in the field of metal corrosion. In this study, we proposed a computational model to predict the corrosion rates of copper in the presence of adsorption-type corrosion inhibitors using density functional theory calculations, microkinetic simulation, and machine learning. The model-calculated corrosion current and potential of clean copper are close to values obtained in available experiments. The copper corrosion rates in the presence of inhibitors were further predicted using the adsorption free energy of adsorbed inhibitors and the inhibitor concentration in solution to describe the effects of inhibitors. The proposed model was applied to predict corrosion inhibition efficiency by combining it with a machine learning model. The combining model exhibited that it was more interpretative and accurate than a machine-learning-only model in predicting corrosion inhibition efficiencies of organic compounds on copper.