Atom Probe Tomography Interlaboratory Study on Clustering Analysis in Experimental Data Using the Maximum Separation Distance Approach

Abstract

We summarize the findings from an interlaboratory study conducted between ten international research groups and investigate the use of the commonly used maximum separation distance and local concentration thresholding methods for solute clustering quantification. The study objectives are: to bring clarity to the range of applicability of the methods; identify existing and/or needed modifications; and interpretation of past published data. Participants collected experimental data from a proton-irradiated 304 stainless steel and analyzed Cu-rich and Ni–Si rich clusters. The datasets were also analyzed by one researcher to clarify variability originating from different operators. The Cu distribution fulfills the ideal requirements of the maximum separation method (MSM), namely a dilute matrix Cu concentration and concentrated Cu clusters. This enabled a relatively tight distribution of the cluster number density among the participants. By contrast, the group analysis of the Ni–Si rich clusters by the MSM was complicated by a high Ni matrix concentration and by the presence of Si-decorated dislocations, leading to larger variability among researchers. While local concentration filtering could, in principle, tighten the results, the cluster identification step inevitably maintained a high scatter. Recommendations regarding reporting, selection of analysis method, and expected variability when interpreting published data are discussed.

Atom Probe Tomography: Development and Application to the Geosciences

“…In zircon, several studies have reported the presence of clusters of Y, Pb and Al (Valley et al , , Peterman et al , Piazolo et al , Blum et al , Peterman et al ). Isolation of these clusters, using 3D volumes defined by isoconcentration surfaces (Hellman et al ) or cluster analysis routines (e.g., Stephenson et al , Dong et al ), significantly reduces the analytical background and permits quantification of compositional and isotopic analysis of these clusters. 207 Pb/ 206 Pb measurements of clusters in both concordant and discordant zircon (Figure ) have been shown to be distinct from bulk isotopic compositions outside the cluster and this has allowed the isotopic compositions of the clusters to be used to constrain the timing of geological events (Valley et al , , Peterman et al ).…”

Three dimensional cluster analysis for atom probe tomography using Ripley’s K-function and machine learning

“…It is informative to compare the estimates in Table 4 to estimates of the same parameters using the MSA, which is one of the most commonly used cluster identification tools in the APT community [12,13,14]. The MSA requires two user-defined input parameters: d max , the maximum distance between two points in a cluster, and N min , the minimum number of points in a cluster [13].…”

“…For example, the maximum separation algorithm (MSA), one of the most commonly used cluster detection algorithms in the APT community, requires the initialization of two crucial parameters: d max (the maximum distance between two points in a cluster) and N min (the minimum number of points in a cluster) [12]. This algorithm's output is highly sensitive to the choice of these two parameters [13,14], and while there are general guidelines for this parameter selection [10,12], the procedures are still subjective. This user-guided parameter selection is typical of other cluster detection algorithms as well [7,9].…”

Revealing Solute Clusters in Coalescence by Atom Probe Tomography Analysis

“…In this study, we selected the maximum separation method (MSM) as an example to implement the methodology, because the MSM, embedded in the IVAS software, is widely used for cluster identification in the APT community and suitable for analyzing clusters in a dilute alloy (Hyde et al, 2011a;Dong et al, 2019). The basic idea is not confined to the MSM, but is applicable to all other cluster search algorithms, as long as the spacing information of the identified clusters is carefully extracted and registered.…”

“…With such information, we can systemically investigate the size and population evolution of these clusters (in comparison with other isolated clusters) with different aging conditions and address the clusters in coalescence using the ex situ technique. In this study, we selected the maximum separation method (MSM) as an example to implement the methodology, because the MSM, embedded in the IVAS software, is widely used for cluster identification in the APT community and suitable for analyzing clusters in a dilute alloy (Hyde et al, 2011 a ; Dong et al, 2019). The basic idea is not confined to the MSM, but is applicable to all other cluster search algorithms, as long as the spacing information of the identified clusters is carefully extracted and registered.…”