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Related Experiment Video

Updated: Sep 17, 2025

Atom Probe Tomography Studies on the CuIn,GaSe2 Grain Boundaries
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Compositional Community Detection: Automated Identification of Chemical Segregation in Atom Probe Tomography Data.

Jenna A Bilbrey1, Christina Doty1, Mark G Wirth1

  • 1National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99352, USA.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
|June 30, 2025
PubMed
Summary
This summary is machine-generated.

We developed Compositional Community Detection (CCD), an unsupervised method for identifying chemical patterns in atom probe tomography data. This technique reveals material compositions and segregation, aiding in the analysis of advanced materials.

Keywords:
atom probe tomographydata analyticsradiation-induced segmentationunsupervised clustering

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Area of Science:

  • Materials Science
  • Data Science
  • Nanotechnology

Background:

  • Atom Probe Tomography (APT) generates complex 3D chemical maps.
  • Analyzing these datasets to identify chemical motifs and segregation is challenging.
  • Existing methods may require supervision or lack interpretability.

Purpose of the Study:

  • Introduce a fully unsupervised clustering method, Compositional Community Detection (CCD).
  • Enable the identification of chemical motifs and segregation patterns in APT data.
  • Provide interpretable descriptors for material characterization.

Main Methods:

  • APT point clouds are segmented into overlapping spherical neighborhoods.
  • Repeated k-means clustering and Louvain community detection group neighborhoods by ion composition.
  • Kolmogorov-Smirnov statistics quantify ion enrichment/depletion within communities.

Main Results:

  • CCD successfully identified chromium carbide and Ni-Si-rich precipitates in irradiated 316 stainless steel.
  • A grain boundary was located via Ni and Si enrichment.
  • Spatial analysis revealed Fe depletion flanking chromium carbide precipitates.

Conclusions:

  • CCD is a powerful unsupervised tool for analyzing chemical segregation in APT data.
  • The method offers interpretable insights into material composition and structure.
  • CCD has broad applicability for diverse materials and extreme environments.