SKM 2021 – wissenschaftliches Programm

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MM: Fachverband Metall- und Materialphysik

MM 7: Topical Session Interface-Dominated Phenomena - Segregation and Embrittlement

MM 7.2: Vortrag

Mittwoch, 29. September 2021, 11:45–12:00, H8

Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data — •Xuyang Zhou^1,2, Ye Wei¹, Markus Kühbach^1,3, Huan Zhao¹, Florian Vogel⁴, Reza Darvishi Kamachali⁵, Gregory B. Thompson², Dierk Raabe¹, and Baptiste Gault^1,6 — ¹Max-Planck-Institut für Eisenforschung GmbH, Düsseldorf, Germany — ²Department of Metallurgical & Materials Engineering, The University of Alabama, Tuscaloosa, USA — ³Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany — ⁴Institute of Advanced Wear & Corrosion Resistant and Functional Materials, Jinan University, Guangzhou, China — ⁵Federal Institute for Materials Research and Testing (BAM), Berlin, Germany — ⁶Department of Materials, Royal School of Mines, Imperial College London, London, UK

The structures of grain boundaries (GBs) have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations.