In silico screening of modulators of magnesium dissolution. (February 2020)

• Record Type: Journal Article
• Authors: Feiler, Christian; Mei, Di; Vaghefinazari, Bahram; Würger, Tim; Meißner, Robert H.; Luthringer-Feyerabend, Bérengère J.C.; Winkler, David A.; Zheludkevich, Mikhail L.; Lamaka, Sviatlana V.
• Is Part Of: Corrosion science. Volume 163(2020)
• Publication Date: 2020-02
• Subjects: Magnesium -- Corrosion modulators -- Density functional theory -- QSPR; Corrosion and anti-corrosives -- Periodicals; 620.11223
• DOI: 10.1016/j.corsci.2019.108245
• Languages: English
• ISSNs: 0010-938X

Abstract

Graphical abstract: Highlights: Artificial neural network predicts performance of corrosion modulating agents for Mg. Calculated HOMO LUMO gap correlates well with inhibition efficiencies for CP Mg. Model predictions validated by subsequent investigation of untested compounds. Trained model facilitates performance prediction for material of similar composition. Abstract: The vast number of small molecules with potentially useful dissolution modulating properties (inhibitors or accelerators) renders currently used experimental discovery methods time- and resource-consuming. Fortunately, emerging computer-assisted methods can explore large areas of chemical space with less effort. Here we show how density functional theory calculations and machine learning methods can work synergistically to generate robust and predictive models that recapitulate experimentally-derived corrosion inhibition efficiencies of small organic compounds for pure magnesium. We further validate our methods by predicting a priori the corrosion modulation properties of seven hitherto untested small molecules and confirm the prediction in subsequent experiments.