Data Science Based Mg Corrosion Engineering

Würger, Tim; Feiler, Christian; Musil, Félix; Feldbauer, Gregor; Höche, Daniel; Lamaka, Sviatlana V.; Zheludkevich, Mikhail L.; Meißner, Robert H.

doi:10.3389/fmats.2019.00053

Cited by 39 publications

(31 citation statements)

References 37 publications

(55 reference statements)

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“…Again, an estimate for the energy barrier of Mg dissolution can be derived from experimental results according to Eqs. (10) and (11). Assuming a prefactor ν = 1 × 10 13 Hz and an anodic exchange current density i 0,Mg = 1.6 × 10 −5 A cm −2 , the reaction barrier can be estimated to 0.85 eV 40,46 .…”

Section: Magnesium Dissolution Analogously To the Cathodic Her The mentioning

confidence: 99%

“…There are several approaches to protect magnesium from corrosion, including alloying and surface coatings 7,8 . Furthermore, the introduction of magnesium dissolution modulators [9][10][11][12][13] , chemical compounds that inhibit or promote magnesium corrosion, facilitates tuning of the corrosion rate to meet application-specific demands. The latter approach allows for tailored degradation rates of resolvable medical implants (e.g.…”

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confidence: 99%

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A first-principles analysis of the charge transfer in magnesium corrosion

Würger

Feiler

Vonbun-Feldbauer

et al. 2020

Sci Rep

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Magnesium is the lightest structural engineering material and bears high potential to manufacture automotive components, medical implants and energy storage systems. However, the practical use of untreated magnesium alloys is restricted as they are prone to corrosion. An essential prerequisite for the control or prevention of the degradation process is a deeper understanding of the underlying corrosion mechanisms. Prior investigations of the formation of gaseous hydrogen during the corrosion of magnesium indicated that the predominant mechanism for this process follows the Volmer–Heyrovský rather than the previously assumed Volmer–Tafel pathway. However, the energetic and electronic states of both reaction paths as well as the charge state of dissolved magnesium have not been fully unraveled yet. In this study, density functional theory calculations were employed to determine these parameters for the Volmer, Tafel and Heyrovský steps to gain a comprehensive understanding of the major corrosion mechanisms responsible for the degradation of magnesium.

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Section: Magnesium Dissolution Analogously To the Cathodic Her The mentioning

confidence: 99%

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confidence: 99%

A first-principles analysis of the charge transfer in magnesium corrosion

Würger

Feiler

Vonbun-Feldbauer

et al. 2020

Sci Rep

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“…Experimental approaches alone cannot possibly explore more than a tiny fraction of the vast space of compounds with potentially useful dissolution modulating properties, despite impressive developments in high throughput techniques [29][30][31][32] . Fortunately, datadriven computational methods [33][34][35][36][37][38][39][40] can efficiently explore larger areas of chemical space with orders of magnitude less time and effort. Hence, they offer a very efficient way to preselect a short list of promising candidates prior to experimental investigation.…”

Section: Introductionmentioning

confidence: 99%

“…A combination of experimental and computational methods constitutes a sound foundation for a data-driven discovery of modulators. Machine learning techniques that model complex quantitative structure-property relationships can predict target properties of hitherto unsynthesized or untested compounds [33][34][35]47,48 . These methods require large, reliable, chemically diverse and balanced training data sets to make the most accurate predictions that can be generalizable to a broad range of materials.…”

Section: Introductionmentioning

confidence: 99%

“…In an extensive experimental study employing hydrogen evolution experiments, Lamaka et al measured the corrosion inhibition performance of over 150 organic compounds for nine distinct Mg-based materials 21 . Previously, a workflow was developed for modeling the experimentally-derived corrosion inhibition values in this database using high-throughput calculations and machine learning algorithms 34 . It was demonstrated in recent works 33,49 that a single descriptor alone cannot adequately describe the complexity of materials degradation.…”

Section: Introductionmentioning

confidence: 99%

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Exploring structure-property relationships in magnesium dissolution modulators

et al. 2021

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Small organic molecules that modulate the degradation behavior of Mg constitute benign and useful materials to modify the service environment of light metal materials for specific applications. The vast chemical space of potentially effective compounds can be explored by machine learning-based quantitative structure-property relationship models, accelerating the discovery of potent dissolution modulators. Here, we demonstrate how unsupervised clustering of a large number of potential Mg dissolution modulators by structural similarities and sketch-maps can predict their experimental performance using a kernel ridge regression model. We compare the prediction accuracy of this approach to that of a prior artificial neural networks study. We confirm the robustness of our data-driven model by blind prediction of the dissolution modulating performance of 10 untested compounds. Finally, a workflow is presented that facilitates the automated discovery of chemicals with desired dissolution modulating properties from a commercial database. We subsequently prove this concept by blind validation of five chemicals.

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Multiscale Computational Approaches toward the Understanding of Materials

Bordonhos

Galvão

Gomes

et al. 2022

Advcd Theory and Sims

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Herewith, an overview of the group's collaborative research efforts on the development and deployment of computational approaches to understand materials and tools at different length and time scales is presented. The techniques employed range from quantum mechanical approaches based on the density functional theory to classical atomistic and coarse-grained force field methods, targeting molecular systems composed of a few to several million atoms at different levels of detail. These new tools and molecular models are presented to the computational materials science community so they can be used in more realistic molecular modelling studies of the properties of materials and their dependence on subtle modifications of their structures. The review concludes by presenting a selection of recent computational case-studies oriented toward the understanding of the synthesis of materials, the interpretation of unexpected experimental results, the prediction of material properties, and the materials selection based on their characteristics for applications in areas such as gas adsorption/separation, corrosion protection, and catalysis.

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Data Science Based Mg Corrosion Engineering

Cited by 39 publications

References 37 publications

A first-principles analysis of the charge transfer in magnesium corrosion

A first-principles analysis of the charge transfer in magnesium corrosion

Exploring structure-property relationships in magnesium dissolution modulators

Multiscale Computational Approaches toward the Understanding of Materials

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