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Related Experiment Video

Updated: Jan 28, 2026

Atom Probe Tomography Analysis of Exsolved Mineral Phases

Atom Probe Tomography Analysis of Exsolved Mineral Phases

Published on: October 25, 2019

Hierarchical density-based cluster analysis framework for atom probe tomography data.

I Ghamarian¹, E A Marquis¹

¹Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA.

Ultramicroscopy

|March 2, 2019

Summary

This summary is machine-generated.

A new hierarchical density-based cluster analysis framework improves atom probe tomography (APT) solute cluster identification. This robust method overcomes limitations of the maximum separation technique, offering enhanced versatility for materials science research.

Keywords:

Atom probe tomography Cluster search DeBaCl Density-based clustering HDBSCAN Level set tree

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Area of Science:

Materials Science
Data Analysis
Computational Methods

Background:

Atom probe tomography (APT) visualizes solute clusters.
Current analysis methods like maximum separation have limitations in parameter selection and applicability.
There is a need for more robust and versatile APT data analysis tools.

Purpose of the Study:

To implement a novel framework for robust and versatile cluster analysis in APT data.
To address the limitations of existing methods for solute cluster identification.
To provide an improved, objective approach for analyzing APT datasets.

Main Methods:

Implementation of a hierarchical density-based cluster analysis framework.
Utilizing HDBSCAN for initial data segmentation and DeBaCl for cluster refinement.
Quantifying cluster stability and atomic probability for refined analysis.
Optional k-nearest neighbor algorithm for matrix atom re-assignment.

Main Results:

The developed framework offers a more objective and versatile approach to cluster analysis in APT.
Performance was validated using synthetic APT datasets, showing improved outcomes compared to the maximum separation method.
The method requires minimal parameter tuning, with only minimum cluster size being essential.

Conclusions:

The hierarchical density-based cluster analysis framework provides a significant advancement for APT data interpretation.
This method enhances the reliability and applicability of solute cluster visualization and quantification.
Open-source code and data facilitate adoption and further development in the APT community.