Skip to main navigation Skip to search Skip to main content

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
  • The University of Sydney
  • University of Oxford
  • Max Planck Institute for Iron Research
  • Deakin University

Research output: Contribution to journalArticlepeer-review

65 Scopus citations

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.

Original languageEnglish
Pages (from-to)324-337
Number of pages14
JournalUltramicroscopy
Volume159
DOIs
StatePublished - Dec 1 2015

Keywords

  • Atom probe tomography
  • Clustering
  • Crystallography
  • Data mining
  • Microscopy
  • Short range order

Fingerprint

Dive into the research topics of 'Mining information from atom probe data'. Together they form a unique fingerprint.

Cite this