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Extracting features buried within high density atom probe point cloud data through simplicial homology.

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Summary

This study introduces a data-driven method using persistent simplicial homology to objectively identify features in Atom Probe Tomography (APT) data. This approach overcomes visual limitations, accurately characterizing precipitates and their boundaries in materials science.

Keywords:
APTAlMgScHomologyImagingPoint cloud dataPrecipitatesTopology

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Area of Science:

  • Materials Science
  • Data Analysis
  • Computational Methods

Background:

  • Atom Probe Tomography (APT) data analysis often relies on subjective visual inspection of iso-concentration surfaces.
  • High data density in APT (~10^7 atoms) can obscure important subsurface features.
  • Current methods lack objectivity in determining concentration thresholds for phase classification.

Purpose of the Study:

  • To develop an objective, data-driven methodology for feature extraction in APT data.
  • To utilize persistent simplicial homology for topological mapping of APT datasets.
  • To accurately classify material phases and identify nanoscale features.

Main Methods:

  • Application of persistent simplicial homology to map the topology of APT data.
  • Objective determination of concentration thresholds for phase classification.
  • Case study on Sc precipitates in an Al-Mg-Sc alloy.

Main Results:

  • Successfully identified Sc precipitates and Al segregation at cluster boundaries.
  • Demonstrated precise demarcation of features not easily resolved by visual methods.
  • Provided an objective approach to threshold selection in APT data analysis.

Conclusions:

  • Persistent simplicial homology offers a powerful, objective tool for APT data analysis.
  • This method enhances the ability to characterize nanoscale features and phase distributions.
  • The technique improves the reliability and accuracy of materials characterization using APT.