Morphological classification of dense objects in atom probe tomography data.

The technique of atom probe tomography is often used to image solute clusters and solute atom segregation to dislocation lines in structural alloys. Quantitative analysis, however, remains a common challenge. To address this gap, we combined a cluster finding algorithm, a skeleton finder algorithm, and morphological classification of dense objects to distinguish solute clusters from solute-decorated dislocation lines, both being characterized by high solute atom densities. The proposed workflow is packaged into a graphical user interface available through GitHub. We illustrate its application on a synthetic dataset containing known objects and apply it to an experimental dataset obtained from a proton-irradiated Alloy 625 that contains high densities of Si-decorated dislocations and Si-rich clusters.

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

Atom probe tomography (APT) has enabled the direct visualization of solute clusters. However one of the main analysis methods used by the APT community, i.e. the maximum separation method, often suffers from subjective parametric selection and limited applicability. To address the need for more robust and versatile analysis tools, a framework based on hierarchical density-based cluster analysis is implemented. Cluster analysis begins with the HDBSCAN algorithm to conservatively segment the datasets into regions containing clusters and a matrix or noise region. The stability of each cluster and the probability that an atom belongs to a cluster are quantified. Each clustered region is further analyzed by the DeBaCl algorithm to separate and refine clusters present in the sub-volumes. Finally, the k-nearest neighbor algorithm may be used to re-assign matrix atoms to clusters, based on their probability values. Four mandatory parameters are required for this cluster analysis approach. However, the selection of an appropriate value for only one of these parameters, i.e. a rough estimate of the minimum cluster size, is essential. The improved performance of the method was evaluated by analyzing four synthetic APT datasets and comparing the outcome with the commonly-used maximum separation method. Codes and data are made available through GitHub.