Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.2612
Publisher DOI: 10.1016/j.corsci.2019.108245
Title: In silico screening of modulators of magnesium dissolution
Language: English
Authors: Feiler, Christian 
Mei, Di 
Vaghefinazari, Bahram 
Würger, Tim 
Meißner, Robert  
Luthringer-Feyerabend, Bérengère J.C. 
Winkler, David A. 
Zheludkevich, Mikhail L. 
Lamaka, Sviatlana V. 
Keywords: Corrosion modulators;Density functional theory;Magnesium;QSPR
Issue Date: Feb-2020
Publisher: Elsevier
Source: Corrosion Science (163): 108245 (2020-02-01)
Journal: Corrosion science 
Abstract (english): 
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.
URI: http://hdl.handle.net/11420/4682
DOI: 10.15480/882.2612
ISSN: 0010-938X
Institute: Kunststoffe und Verbundwerkstoffe M-11 
Molekulardynamische Simulation weicher Materie M-EXK2 
Document Type: Article
Project: 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.
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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