Traditionally many structural alloys are protected by primer coatings loaded with corrosion inhibiting additives. Strontium Chromate (or other chromates) have been shown to be extremely effectively inhibitors, and find extensive use in protective primer formulations. Unfortunately, hexavalent chromium which imbues these coatings with their corrosion inhibiting properties is also highly toxic, and their use is being increasingly restricted by legislation. In this work we explore a novel tridimensional Quantitative-Structure Property Relationship (3D-QSPR) approach, comparative molecular surface analysis (CoMSA), which was developed to recognize "high-performing" corrosion inhibitor candidates from the distributions of electronegativity, polarizability and van der Waals volume on the molecular surfaces of 28 small organic molecules. Multivariate statistical analysis identified five prototypes molecules, which are capable of explaining 71% of the variance within the inhibitor data set; whilst a further five molecules were also identified as archetypes, describing 75% of data variance. All active corrosion inhibitors, at a 80% threshold, were successfully recognized by the CoMSA model with adequate specificity and precision higher than 70% and 60%, respectively. The model was also capable of identifying structural patterns, that revealed reasonable starting points for where structural changes may augment corrosion inhibition efficacy. The presented methodology can be applied to other functional molecules and extended to cover structure-activity studies in a diverse range of areas such as drug design and novel material discovery.
Keywords: 3D-QSAR; Corrosion inhibition; DFT methods; Data analytics.
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