Testing Outlier Detection Algorithms for Identifying Early-Stage Solute Clusters in Atom Probe Tomography

Abstract: Atom probe tomography is commonly used to study solute clustering and precipitation in materials. However, standard techniques, such as the density based spatial clustering applications with noise (DBSCAN) perform poorly with respect to small clusters of less than 25 atoms. This is a fundamental limitation of density-based clustering techniques due to the usage of Nmin, an arbitrary lower limit placed on cluster sizes. Therefore, this paper attempts to consider atom probe clustering as an outlier detection problem of which KNN, LOF, LUNAR algorithms were tested against a simulated dataset and compared to the standard method, for a range of cluster sizes. The decision score output of the algorithms was then auto thresholded by the Karcher mean to remove human bias. Each of the major models tested outperforms DBSCAN for cluster sizes of less than 25 atoms but underperforms for sizes greater than 30 atoms. However, the new combined kNN and DBSCAN method presented was able to perform well at all cluster sizes. The combined kNN and DBSCAN method is presented as a possible new standard approach to identifying solute clusters in atom probe tomography.