Detecting Clusters in Atom Probe Data with Gaussian Mixture Models. (26th April 2017)

• Record Type: Journal Article
• Authors: Zelenty, Jennifer; Dahl, Andrew; Hyde, Jonathan; Smith, George D. W.; Moody, Michael P.
• Editors: Thuvander, Mattias; Cairney, Julie; Gerstl, Stephan
• Is Part Of: Microscopy and microanalysis. Volume 23:Number 2(2017)
• Page Start: 269
• Page End: 278
• Publication Date: 2017-04-26
• Subjects: atom probe tomography, -- cluster identification, -- Gaussian mixture models, -- expectation maximization, -- machine learning; Microscopy -- Periodicals; Microchemistry -- Periodicals; 502.82
• DOI: 10.1017/S1431927617000320
• Languages: English
• ISSNs: 1431-9276

Abstract

Abstract: Accurately identifying and extracting clusters from atom probe tomography (APT) reconstructions is extremely challenging, yet critical to many applications. Currently, the most prevalent approach to detect clusters is the maximum separation method, a heuristic that relies heavily upon parameters manually chosen by the user. In this work, a new clustering algorithm, Gaussian mixture model Expectation Maximization Algorithm (GEMA), was developed. GEMA utilizes a Gaussian mixture model to probabilistically distinguish clusters from random fluctuations in the matrix. This machine learning approach maximizes the data likelihood via expectation maximization: given atomic positions, the algorithm learns the position, size, and width of each cluster. A key advantage of GEMA is that atoms are probabilistically assigned to clusters, thus reflecting scientifically meaningful uncertainty regarding atoms located near precipitate/matrix interfaces. GEMA outperforms the maximum separation method in cluster detection accuracy when applied to several realistically simulated data sets. Lastly, GEMA was successfully applied to real APT data.