A Machine Learning Framework for Quantifying Chemical Segregation and Microstructural Features in Atom Probe Tomography Data

Microscopy and Microanalysis, vol. 29, pp. 1658–1670

Abstract

Atom probe tomography (APT) is ideally suited to characterize and understand the interplay of segregation and microstructure in modern multi-component materials. Yet, the quantitative analysis typically relies on human expertise to define regions of interest. We introduce a computationally efficient, multi-stage machine learning strategy to identify compositionally distinct domains in a semi-automated way, and subsequently quantify their geometric and compositional characteristics. In our algorithmic pipeline, we first coarse-grain the APT data into voxels, collect the composition statistics, and decompose it via clustering in composition space. The composition classification then enables the real-space segmentation via a density-based clustering algorithm, thus revealing the microstructure at voxel resolution. Our approach is demonstrated for a Sm-(Co,Fe)-Zr-Cu alloy. The alloy exhibits two precipitate phases with a plate-like, but intertwined morphology. The primary segmentation is further refined to disentangle these geometrically complex precipitates into individual plate-like parts by an unsupervised approach based on principle component analysis, or a U-Net-based semantic segmentation trained on the former. Following the composition and geometric analysis, detailed composition distribution and segregation effects relative to the predominant plate-like geometry can be readily mapped from the point cloud, without resorting to the voxel compositions.

Authors 10

1. Alaukik Saxena

   Max-Planck-Institut für Nachhaltige Materialien

   Affiliation as printed

   Max-Planck-Institut für Eisenforschung GmbH , Max-Planck-Straße 1, 40237 Düsseldorf , Germany

2. Nikita Polin

   Max-Planck-Institut für Nachhaltige Materialien

   Affiliation as printed

   Max-Planck-Institut für Eisenforschung GmbH , Max-Planck-Straße 1, 40237 Düsseldorf , Germany

3. Navyanth Kusampudi

   Max-Planck-Institut für Nachhaltige Materialien

   Affiliation as printed

   Max-Planck-Institut für Eisenforschung GmbH , Max-Planck-Straße 1, 40237 Düsseldorf , Germany

4. Shyam Katnagallu

   Max-Planck-Institut für Nachhaltige Materialien

   Affiliation as printed

   Max-Planck-Institut für Eisenforschung GmbH , Max-Planck-Straße 1, 40237 Düsseldorf , Germany

5. Leopoldo Molina‐Luna

   Technische Universität Darmstadt

   Affiliation as printed

   Department of Materials and Earth Sciences, Technische Universität Darmstadt, Peter-Grünberg-Straße 2, 64287 Darmstadt, Germany

6. Oliver Gutfleisch

   Technische Universität Darmstadt

   Affiliation as printed

   Functional Materials, Institute of Materials Science, Technical University of Darmstadt, Alarich-Weiss-Straße 16, 64287 Darmstadt, Germany

7. Benjamin Berkels Aachen Aachen Institute for Advanced Study in Computational Engineering Science (AICES)

   Aachen Institute for Advanced Study in Computational Engineering Science · RWTH Aachen University

   Affiliation as printed

   Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University , Schinkelstr. 2, 52062 Aachen, Germany

8. Baptiste Gault

   Max-Planck-Institut für Nachhaltige Materialien · Imperial College London

   Affiliation as printed

   Department of Materials, Royal School of Mines, Imperial College London, SW7 2AZ London, UK

   Max-Planck-Institut für Eisenforschung GmbH , Max-Planck-Straße 1, 40237 Düsseldorf , Germany

9. Jörg Neugebauer

   Max-Planck-Institut für Nachhaltige Materialien

   Affiliation as printed

   Max-Planck-Institut für Eisenforschung GmbH , Max-Planck-Straße 1, 40237 Düsseldorf , Germany

10. Christoph Freysoldt corresponding

    Max-Planck-Institut für Nachhaltige Materialien

    Affiliation as printed

    Max-Planck-Institut für Eisenforschung GmbH , Max-Planck-Straße 1, 40237 Düsseldorf , Germany