Mining information from atom probe data

Julie M Cairney; Krishna Rajan; Daniel Haley; Baptiste Gault; Paul A J Bagot; Pyuck-Pa Choi; Peter J Felfer; Simon P Ringer; Ross K W Marceau; Michael P Moody

doi:10.1016/j.ultramic.2015.05.006

Mining information from atom probe data

Ultramicroscopy. 2015 Dec:159 Pt 2:324-37. doi: 10.1016/j.ultramic.2015.05.006. Epub 2015 May 14.

Authors

Affiliations

¹ School of Aerospace, Mechanical, Mechatronic Engineering, The University of Sydney, NSW 2006, Australia; Australian Centre for Microscopy and Microanalysis, The University of Sydney, NSW 2006, Australia. Electronic address: [email protected].
² Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011, USA.
³ Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, UK; Max Planck Institut für Eisenforschung GmbH, Max-Planck Straße 1, 40237 Düsseldorf, Germany.
⁴ Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, UK.
⁵ Max Planck Institut für Eisenforschung GmbH, Max-Planck Straße 1, 40237 Düsseldorf, Germany.
⁶ School of Aerospace, Mechanical, Mechatronic Engineering, The University of Sydney, NSW 2006, Australia; Australian Centre for Microscopy and Microanalysis, The University of Sydney, NSW 2006, Australia.
⁷ Institute for Frontier Materials, Deakin University, Geelong Technology Precinct, 75 Pigdons Road, Waurn Ponds, Victoria 3216, Australia.

PMID: 26095825
DOI: 10.1016/j.ultramic.2015.05.006

Abstract

Whilst atom probe tomography (APT) is a powerful technique with the capacity to gather information containing hundreds of millions of atoms from a single specimen, the ability to effectively use this information creates significant challenges. The main technological bottleneck lies in handling the extremely large amounts of data on spatial-chemical correlations, as well as developing new quantitative computational foundations for image reconstruction that target critical and transformative problems in materials science. The power to explore materials at the atomic scale with the extraordinary level of sensitivity of detection offered by atom probe tomography has not been not fully harnessed due to the challenges of dealing with missing, sparse and often noisy data. Hence there is a profound need to couple the analytical tools to deal with the data challenges with the experimental issues associated with this instrument. In this paper we provide a summary of some key issues associated with the challenges, and solutions to extract or "mine" fundamental materials science information from that data.

Keywords: Atom probe tomography; Clustering; Crystallography; Data mining; Microscopy; Short range order.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.