|Publisher DOI:||10.1038/s41529-020-00148-z||Title:||Exploring structure-property relationships in magnesium dissolution modulators||Language:||English||Authors:||Würger, Tim
Winkler, David A.
Lamaka, Sviatlana V.
Zheludkevich, Mikhail L.
|Issue Date:||8-Jan-2021||Publisher:||Macmillan Publishers Limited, part of Springer Nature||Source:||npj Materials Degradation 5 (1): 2 (2021-12-01)||Journal:||npj Materials degradation||Abstract (english):||
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.
|URI:||http://hdl.handle.net/11420/9657||DOI:||10.15480/882.3579||ISSN:||2397-2106||Institute:||Kunststoffe und Verbundwerkstoffe M-11||Document Type:||Article||Funded by:||Deutscher Akademischer Austauschdienst (DAAD)
Deutsche Forschungsgemeinschaft (DFG)
|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). T.W., D.A.W., and C.F. gratefully acknowledge funding by the Deutscher Akademischer Austauschdienst (DAAD, German Academic Exchange Service) via Projektnummer 57511455. R.M. gratefully acknowledge funding by the Deutsche Forschungsgemeinschaft (D.F.G., German Research Foundation) via Projektnummer 192346071—SFB 986 and Projektnummer 390794421—GRK 2462.||Project:||Projekt DEAL||License:||CC BY 4.0 (Attribution)|
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