Large-Scale Atom Probe Tomography Data Mining: Methods and Application to Inform Hydrogen Behavior

A large number of atom probe tomography (APT) datasets from past experiments were collected into a database to conduct statistical analyses. An effective way of handling the data is shown, and a study on hydrogen is conducted to illustrate the usefulness of this approach. We propose to handle a large collection of APT spectra as a point cloud and use a city block distance-based metric to measure dissimilarity between spectra. This enables quick and automated searching for spectra by similarity. Since spectra from APT experiments on similar materials are similar, the point cloud of spectra contains clusters. Analysis of these clusters of spectra in this point cloud allows us to infer the sample materials. The behavior of contaminant hydrogen is analyzed and correlated with voltage, electric field, and sample base material. Across several materials, the H2+ /H+ ratio is found to decrease with increasing field, likely an indication of postionization of H2+ ions. The absolute amounts of H2+ and H+ are found to frequently increase throughout APT experiments.

Automated summary

A large collection of atom probe tomography (APT) datasets from past experiments were gathered into a database for statistical analyses. A method of treating the data as point clouds and using a city block distance-based metric to compare spectra was proposed, allowing for quick and automated searching for similar spectra. The clustering of spectra in the point cloud was found to provide valuable insights into the sample materials. A study on hydrogen behavior revealed correlations with voltage, electric field, and sample base material, indicating potential postionization of H2+ ions and frequent increases in the absolute amounts of H2+ and H+ throughout APT experiments.