Please use this identifier to cite or link to this item:
Publisher DOI: 10.3389/fmats.2019.00053
Title: Data science based mg corrosion engineering
Language: English
Authors: Würger, Tim 
Feiler, Christian 
Musil, Félix 
Feldbauer, Gregor  
Höche, Daniel 
Lamaka, Sviatlana V. 
Zheludkevich, Mikhail L. 
Meißner, Robert  
Issue Date: 8-Mar-2019
Source: Frontiers in Materials (6): 1-9 (2019-03-08)
Journal: Frontiers in Materials 
Abstract (english): 
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. © 2019 Würger, Feiler, Musil, Feldbauer, Höche, Lamaka, Zheludkevich and Meißner.
DOI: 10.15480/882.2265
ISSN: 2296-8016
Institute: Kunststoffe und Verbundwerkstoffe M-11 
Keramische Hochleistungswerkstoffe M-9 
Molekulardynamische Simulation weicher Materie M-EXK2 
Document Type: Article
Project: SFB 986: Teilprojekt A8 - Molekulardynamische Simulation der Selbstassemblierung von polymerbeschichteten keramischen Nanopartikeln 
SFB 986: Teilprojekt A4 - Ab-initio basierende Modellierung und Beeinflussung der mechanischen Eigenschaften von Hybridgrenzflächen 
More Funding information: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Projektnummer 192346071-SFB 986.
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
Appears in Collections:Publications with fulltext

Files in This Item:
File Description SizeFormat
fmats-06-00053.pdfVerlagsversion1,06 MBAdobe PDFView/Open
Show full item record

Page view(s)

Last Week
Last month
checked on Dec 8, 2021


checked on Dec 8, 2021


Last Week
Last month
checked on Dec 4, 2021

Google ScholarTM


Note about this record

Cite this record


This item is licensed under a Creative Commons License Creative Commons