Abstract
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.
Tim Würger
Doktorand
Computergestützte Materialwissenschaften, Atomistische Simulation & Modellierung, Maschinelles Lernen und datengetriebene Ansätze für Materialmodellierung & -design
Mikhail Zheludkevich
Professor
Elektrochemie, Multifunktionale Oberflächen & Aktiver Schutz von Leichtmetallen, Multi-Material-Systeme
Robert Meißner
Professor
Molekulardynamische Simulationen von Grenzflächen, Berechnung freier Energien biomolekularer und elektrochemischer Systeme, atomistische Betrachtung der Magnesiumkorrosion, Entwicklung datengetriebener Modelle zur Indentifikation von (umweltfreundlichen) Degradationsmodulatoren
Christian Feiler
Postdoc & MLE-Koordinator
Atomistische Simulation & Modellierung, Entwicklung von Quantitativen Struktur-Eigenschafts-Beziehungsmodellen