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Cairney JM, Rajan K, Haley D, Gault B, Bagot PAJ, Choi PP, Felfer PJ, Ringer SP, Marceau RKW, Moody MP. Mining information from atom probe data. Ultramicroscopy 2015; 159 Pt 2:324-37. [PMID: 26095825 DOI: 10.1016/j.ultramic.2015.05.006] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2014] [Revised: 05/03/2015] [Accepted: 05/12/2015] [Indexed: 10/23/2022]
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.
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Affiliation(s)
- Julie M Cairney
- 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.
| | - Krishna Rajan
- Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011, USA
| | - Daniel Haley
- 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
| | - Baptiste Gault
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, UK
| | - Paul A J Bagot
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, UK
| | - Pyuck-Pa Choi
- Max Planck Institut für Eisenforschung GmbH, Max-Planck Straße 1, 40237 Düsseldorf, Germany
| | - Peter J Felfer
- 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
| | - Simon P Ringer
- 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
| | - Ross K W Marceau
- Institute for Frontier Materials, Deakin University, Geelong Technology Precinct, 75 Pigdons Road, Waurn Ponds, Victoria 3216, Australia
| | - Michael P Moody
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, UK
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Srinivasan S, Kaluskar K, Broderick S, Rajan K. Extracting features buried within high density atom probe point cloud data through simplicial homology. Ultramicroscopy 2015; 159 Pt 2:374-80. [PMID: 25959554 DOI: 10.1016/j.ultramic.2015.04.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Revised: 03/13/2015] [Accepted: 04/12/2015] [Indexed: 11/29/2022]
Abstract
Feature extraction from Atom Probe Tomography (APT) data is usually performed by repeatedly delineating iso-concentration surfaces of a chemical component of the sample material at different values of concentration threshold, until the user visually determines a satisfactory result in line with prior knowledge. However, this approach allows for important features, buried within the sample, to be visually obscured by the high density and volume (~10(7) atoms) of APT data. This work provides a data driven methodology to objectively determine the appropriate concentration threshold for classifying different phases, such as precipitates, by mapping the topology of the APT data set using a concept from algebraic topology termed persistent simplicial homology. A case study of Sc precipitates in an Al-Mg-Sc alloy is presented demonstrating the power of this technique to capture features, such as precise demarcation of Sc clusters and Al segregation at the cluster boundaries, not easily available by routine visual adjustment.
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Affiliation(s)
- Srikant Srinivasan
- Institute of Combinatorial Discovery, Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011-2300, USA
| | - Kaustubh Kaluskar
- Institute of Combinatorial Discovery, Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011-2300, USA
| | - Scott Broderick
- Institute of Combinatorial Discovery, Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011-2300, USA
| | - Krishna Rajan
- Institute of Combinatorial Discovery, Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011-2300, USA.
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