Dr. Christian Feiler

ZOOM Meeting

May 11th, 2022

4 pm

Chair: Dr. Andey Yachmenev



Dr. Christian Feiler works at the Hereon Institute of Surface Science where he develops quantitative structure-activity relationship models to predict the effects of untested additives on the corrosion behaviour of light metals, especially magnesium alloys. The two recent publications on this topic can be found here and here. His talk is entitled "Predicting the Corrosion Inhibition Efficiencies of Magnesium Dissolution Modulators using Computational Techniques".


Christian Feiler, Tim Würger, Lisa Sahlmann, Robert H. Meißner, Linqian Wang, Darya Snihirova, Elisabeth J. Schiessler, Roland C. Aydin, Christian J. Cyron, David A. Winkler, Di Mei, Bahram Vaghefinazari, Sviatlana V. Lamaka, Daniel Höche, Mikhail L. Zheludkevich

As the lightest structural engineering metal, magnesium (Mg) is a promising base material for advanced technology. However, to unlock the full potential of Mg–based materials, precise control over the corrosion rate is important whereas it was demonstrated that its degradation behaviour can be affected by small organic molecules. Recent research has discovered new, effective magnesium corrosion inhibitors, and electrolyte additives that boost the efficiency of magnesium-air primary batteries. However, as small molecule chemistry space is essentially infinite, efficiently searching it to find small molecules with superior dissolution modulating properties (inhibitors or accelerators) using time- and resource-consuming experimental discovery methods is intractable.
Consequently, computer-assisted selection of the most promising candidates prior to experimental investigation is of great benefit in the search for effective corrosion modulating additives for Mg-based materials. Apart from a sufficiently large, diverse and reliable training data set and a suitable modelling framework (usually based on one or more machine learning algorithms), relevant molecular descriptors are a prerequisite for the development of predictive quantitative structure-property relationship models. The latter can either be selected by chemical intuition or based on statistical methods. The talk outlines our recent activities in the collection of training data sets, systematic selection of relevant input features and the subsequent development of quantitative structure-activity relationship models to predict the effect of untested dissolution modulators on the corrosion behaviour of Mg and its alloys.


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