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.
Christian Feiler
Postdoc & MLE-Koordinator
Atomistische Simulation & Modellierung, Entwicklung von Quantitativen Struktur-Eigenschafts-Beziehungsmodellen
Tim Würger
Doktorand
Computergestützte Materialwissenschaften, Atomistische Simulation & Modellierung, Maschinelles Lernen und datengetriebene Ansätze für Materialmodellierung & -design
Robert Meißner
Professor
Molekulardynamische Simulationen von Grenzflächen, Berechnung freier Energien biomolekularer und elektrochemischer Systeme, atomistische Betrachtung der Magnesiumkorrosion, Entwicklung datengetriebener Modelle zur Indentifikation von (umweltfreundlichen) Degradationsmodulatoren
Mikhail Zheludkevich
Professor
Elektrochemie, Multifunktionale Oberflächen & Aktiver Schutz von Leichtmetallen, Multi-Material-Systeme