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Detecting Clusters in Atom Probe Data with Gaussian Mixture Models

Published online by Cambridge University Press:  26 April 2017

Jennifer Zelenty ,
Andrew Dahl ,
Jonathan Hyde ,
George D. W. Smith  and
Michael P. Moody
Jennifer Zelenty*
Affiliation:
Department of Materials, University of Oxford, Oxford OX1 3PH, UK
Andrew Dahl
Affiliation:
Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
Jonathan Hyde
Affiliation:
National Nuclear Laboratory, Culham Science Centre, Abingdon OX14 3DB, UK
George D. W. Smith
Affiliation:
Department of Materials, University of Oxford, Oxford OX1 3PH, UK
Michael P. Moody
Affiliation:
Department of Materials, University of Oxford, Oxford OX1 3PH, UK
*
*Corresponding author. [email protected]
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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.

Keywords

atom probe tomographycluster identificationGaussian mixture modelsexpectation maximizationmachine learning
Type
New Approaches and Correlative Microscopy
Information
Microscopy and Microanalysis , Volume 23 , Special Issue 2: Atom Probe Tomography & Microscopy 2016 , April 2017 , pp. 269 - 278
Copyright
© Microscopy Society of America 2017 

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