Poster Presentation 24th Australian Conference on Microscopy and Microanalysis 2016

Information theoretic assessment of feature analyses used in atom probe  (#287)

Leigh Stephenson 1 , Simon Ringer 1
  1. The University of Sydney, The University Of Sydney, NSW, Australia
There are many particle and cluster analysis methods in the broader scientific literature, and only a few have been adapted for use in atom probe tomography [1-5]. Heuristics for parameter selection have been developed [6-7] but have been proven in only specific material applications using particular atom probe configurations. We develop a workflow that takes data simulations and uses information theory [8] to assess whether a particular algorithm can be reliably implemented without prior information. We consider this for dilute doping in a regular crystalline matrix, clustering phenomena within dilute solute alloys and large particles within a matrix. This work could easily be easily adapted for any application requiring the analysis of 3D point information or for any new clustering analysis. Moreover, we demonstrate the strengths of a "shared nearest neighbour" cluster analysis [9] which does not require exact scaling within a reconstruction to operate effectively. 
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  2. [2] Williams, C. A., Haley, D., Marquis, E. A., Smith, G. D., & Moody, M. P. (2013). Defining clusters in APT reconstructions of ODS steels. Ultramicroscopy, 132, 271-278.
  3. [3] Samudrala, S., Wodo, O., Suram, S. K., Broderick, S., Rajan, K., & Ganapathysubramanian, B. (2013). A graph-theoretic approach for characterization of precipitates from atom probe tomography data. Computational Materials Science, 77, 335-342.
  4. [4] Srinivasan, S., Kaluskar, K., Broderick, S., & Rajan, K. (2015). Extracting features buried within high density atom probe point cloud data through simplicial homology. Ultramicroscopy.
  5. [5] Felfer, P., Ceguerra, A. V., Ringer, S. P., & Cairney, J. M. (2015). Detecting and extracting clusters in atom probe data: A simple, automated method using Voronoi cells. Ultramicroscopy, 150, 30-36.
  6. [6] Hyde, J. M., Marquis, E. A., Wilford, K. B., & Williams, T. J. (2011). A sensitivity analysis of the maximum separation method for the characterisation of solute clusters. Ultramicroscopy, 111(6), 440-447.
  7. [7] Jägle, E. A., Choi, P. P., & Raabe, D. (2014). The maximum separation cluster analysis algorithm for atom-probe tomography: Parameter determination and accuracy. Microscopy and Microanalysis, 20(06), 1662-1671.
  8. [8] Yao, Y. Y. (2003). Information-theoretic measures for knowledge discovery and data mining. In Entropy Measures, Maximum Entropy Principle and Emerging Applications (pp. 115-136). Springer Berlin Heidelberg.
  9. [9] Ertoz, L., Steinbach, M., & Kumar, V. (2002, April). A new shared nearest neighbor clustering algorithm and its applications. In Workshop on Clustering High Dimensional Data and its Applications at 2nd SIAM International Conference on Data Mining (pp. 105-115).