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DS4S - Data Science for Science

Wednesday, 11.5.2022 - 4 pm

Dr. Christian Feiler will talk about his research

We, the graduate school Data Science in Hamburg - Helmholtz Graduate School for the Structure of Matter (DASHH), the Leibniz ScienceCampus InterACt (LSC InterACt) and the Center for Data and Computing in Natural Sciences (CDCS) are currently organizing a new series of networking events entitled Data Science for Science (DS4S) to enable exchange and networking across disciplines covered in the metropolitan region Hamburg. Therefore, we invite all interested researchers to join the biweekly events which are scheduled on Wednesdays at 4 pm.

Researchers from all partner institutions are invited to present different aspects of (envisioned) data science research and the corresponding application fields. The new series is an extension of our former Hamburg COVID-19 Series, which received a lot of attention and positive feedback, although having a much broader scope now. The event is currently organized online, but we hope that we can extend our series to an on-site event with talks and a subsequent networking event by June.

The next lecture will be presented by Dr. Christian Feiler (Hereon Institute of Surface Science) on May 11th, 2022 at 4 pm. 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".

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.
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

If you want to contribute to this series and looking for collaborations with researchers from Hamburg concerning a specific method or application field, feel free to contact us via the contact button.

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We are especially grateful for the support of this series by the Joachim Herz Foundation.