In silico screening of modulators of magnesium dissolution

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.

Schlagworte

Corrosion modulators

Density functional theory

Magnesium

QSPR

DDC Class

600: Technik

Projekt(e)

SFB 986: Teilprojekt A8 - Molekulardynamische Simulation der Selbstassemblierung von polymerbeschichteten keramischen Nanopartikeln

More Funding Information

Funding by HZG MMDi IDEA project is gratefully acknowledged. DM thanks China Scholarship Council for the award of fellowship and funding (No. 201607040051). RM gratefully acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Projektnumber 192346071 - SFB 986.

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In silico screening of modulators of magnesium dissolution