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Related Experiment Video

Updated: Aug 12, 2025

Atom Probe Tomography Analysis of Exsolved Mineral Phases
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Cluster characterization in atom probe tomography: Machine learning using multiple summary functions.

Roland A Bennett1, Andrew P Proudian1, Jeramy D Zimmerman1

  • 1Department of Physics, Colorado School of Mines, Golden, CO 80401, United States of America.

Ultramicroscopy
|January 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning method using spatial statistics to analyze atom probe tomography data. The new approach accurately estimates material concentrations and clustering parameters, improving data interpretation.

Keywords:
Atom probe tomographyCluster detectionMachine learningSpatial statistics

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Area of Science:

  • Materials Science
  • Data Science
  • Computational Methods

Background:

  • Atom Probe Tomography (APT) is crucial for nanoscale material analysis.
  • Characterizing solute clustering requires robust quantitative methods.
  • Existing methods may lack precision in determining key clustering parameters.

Purpose of the Study:

  • To develop a machine learning (ML) model for characterizing solute clustering in APT data.
  • To improve the accuracy of estimating intracluster concentration (ρc), background concentration (ρb), clustering radius (r̄), and radius dispersity (δr).
  • To enhance the analysis of spatial statistics in APT data.

Main Methods:

  • Utilized a Bayesian regularized neural network trained on simulated APT data.
  • Incorporated multiple spatial statistics summary functions, including Ripley's K-function and nearest-neighbor statistics.
  • Employed a combination of features to train the ML model.

Main Results:

  • Achieved highly accurate estimates for ρc and ρb, with 90% of predictions within 4.0% of true values.
  • Demonstrated significant reductions in root-mean-square errors for ρc (81.5%) and ρb (92.8%) compared to K-function-only methods.
  • Showed improved differentiation between clustering radius (r̄) and radius dispersity (δr) by incorporating nearest-neighbor features.

Conclusions:

  • The developed ML method provides a more accurate and robust characterization of solute clustering in APT data.
  • Integrating diverse spatial statistics significantly enhances the predictive power for concentration-based metrics.
  • This approach offers a valuable tool for quantitative analysis in materials science.