Predicting corrosion inhibition efficiencies of small organic molecules using data-driven techniques

Selecting effective corrosion inhibitors from the vast chemical space is not a trivial task, as it is essentially infinite. Fortunately, machine learning techniques have shown great potential in generating shortlists of inhibitor candidates prior to large-scale experimental testing. In this work, we used the corrosion responses of 58 small organic molecules on the magnesium alloy AZ91 and utilized molecular descriptors derived from their geometry and density functional theory calculations to encode their molecular information. Statistical methods were applied to select the most relevant features to the target property for support vector regression and kernel ridge regression models, respectively, to predict the behavior of untested compounds. The performance of the two supervised learning approaches were compared and the robustness of the data-driven models were assessed by experimental blind testing.