In silico screening of modulators of magnesium dissolution

Feiler, Christian; Mei, Di; Vaghefinazari, Bahram; Würger, Tim; Meißner, Robert H.; Luthringer-Feyerabend, Bérengère; Winkler, David A.; Zheludkevich, Mikhail L.; Lamaka, Sviatlana V.

doi:10.1016/j.corsci.2019.108245

Cited by 45 publications

(45 citation statements)

References 59 publications

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“…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%

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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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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“…This sparse feature selection method is based on L1 regression, similar to the LASSO method 53,54 . This neural network method has been shown to generate robust and optimally sparse models of diverse materials properties [55][56][57][58] .…”

Section: Nonlinear Property Modelingmentioning

confidence: 99%

Machine learning property prediction for organic photovoltaic devices

et al. 2020

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Organic photovoltaic (OPV) materials are promising candidates for cheap, printable solar cells. However, there are a very large number of potential donors and acceptors, making selection of the best materials difficult. Here, we show that machine-learning approaches can leverage computationally expensive DFT calculations to estimate important OPV materials properties quickly and accurately. We generate quantitative relationships between simple and interpretable chemical signature and one-hot descriptors and OPV power conversion efficiency (PCE), open circuit potential (Voc), short circuit density (Jsc), highest occupied molecular orbital (HOMO) energy, lowest unoccupied molecular orbital (LUMO) energy, and the HOMO–LUMO gap. The most robust and predictive models could predict PCE (computed by DFT) with a standard error of ±0.5 for percentage PCE for both the training and test set. This model is useful for pre-screening potential donor and acceptor materials for OPV applications, accelerating design of these devices for green energy applications.

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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%

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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In silico screening of modulators of magnesium dissolution

Cited by 45 publications

References 59 publications

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

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

Machine learning property prediction for organic photovoltaic devices

Exploring structure-property relationships in magnesium dissolution modulators

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