Data Science Based Mg Corrosion Engineering


Magnesium exhibits a high potential for a variety of applications in areas such as transport, energy and medicine. However, untreated magnesium alloys are prone to corrosion, restricting their practical application. Therefore, it is necessary to develop new approaches that can prevent or control corrosion and degradation processes in order to adapt to the specific needs of the application. One potential solution is using corrosion inhibitors which are capable of drastically reducing the degradation rate as a result of interactions with the metal surface or components of the corrosive medium. As the sheer number of potential dissolution modulators makes it impossible to obtain a detailed atomistic understanding of the inhibition mechanisms for each additive, other measures for inhibition prediction are required. For this purpose, a concept is presented that combines corrosion experiments, machine learning, data mining, density functional theory calculations and molecular dynamics to estimate corrosion inhibition properties of still untested molecules. Concomitantly, this approach will provide a deeper understanding of the fundamental mechanisms behind the prevention of corrosion events in magnesium-based materials and enables more accurate continuum corrosion simulations. The presented concept facilitates the search for molecules with a positive or negative effect on the inhibition efficiency and could thus significantly contribute to the better control of magnesium / electrolyte interface properties.

Frontiers in Materials 5:53
Tim Würger

Computergestützte Materialwissenschaften, Atomistische Simulation & Modellierung, Maschinelles Lernen und datengetriebene Ansätze für Materialmodellierung & -design

Christian Feiler
Postdoc & MLE-Koordinator

Atomistische Simulation & Modellierung, Entwicklung von Quantitativen Struktur-Eigenschafts-Beziehungsmodellen

Gregor Vonbun-Feldbauer
Projektleiter & MLE-Koordinator

Computergestützte Materialwissenschaften, Atomistische Simulation & Modellierung, Multiskalenansätze, Maschinelles Lernen und datengetriebene Ansätze für Materialmodellierung & -design, Hybrid- und Mehrkomponentensysteme, Ober- und Grenzflächenphysik

Mikhail Zheludkevich

Elektrochemie, Multifunktionale Oberflächen & Aktiver Schutz von Leichtmetallen, Multi-Material-Systeme

Robert Meißner

Molekulardynamische Simulationen von Grenzflächen, Berechnung freier Energien biomolekularer und elektrochemischer Systeme, atomistische Betrachtung der Magnesiumkorrosion, Entwicklung datengetriebener Modelle zur Indentifikation von (umweltfreundlichen) Degradationsmodulatoren