Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-30T00:00:36.887Z Has data issue: false hasContentIssue false
  • English
  • Français

The Application of the OPTICS Algorithm to Cluster Analysis in Atom Probe Tomography Data

Published online by Cambridge University Press:  08 March 2019

Jing Wang Open the ORCID record for Jing Wang [Opens in a new window] ,
Daniel K. Schreiber ,
Nathan Bailey ,
Peter Hosemann  and
Mychailo B. Toloczko
Jing Wang*
Affiliation:
Pacific Northwest National Laboratory, Energy and Environment Directorate, Richland, WA, 99354, USA
Daniel K. Schreiber
Affiliation:
Pacific Northwest National Laboratory, Energy and Environment Directorate, Richland, WA, 99354, USA
Nathan Bailey
Affiliation:
Department of Nuclear Engineering, University of California, Berkeley, CA, 94720, USA
Peter Hosemann
Affiliation:
Department of Nuclear Engineering, University of California, Berkeley, CA, 94720, USA
Mychailo B. Toloczko
Affiliation:
Pacific Northwest National Laboratory, Energy and Environment Directorate, Richland, WA, 99354, USA
*
*Author for correspondence: Jing Wang, E-mail: [email protected]
Get access

Abstract

Atom probe tomography (APT) is a powerful technique to characterize buried three-dimensional nanostructures in a variety of materials. Accurate characterization of those nanometer-scale clusters and precipitates is of great scientific significance to understand the structure–property relationships and the microstructural evolution. The current widely used cluster analysis method, a variant of the density-based spatial clustering of applications with noise algorithm, can only accurately extract clusters of the same atomic density, neglecting several experimental realities, such as density variations within and between clusters and the nonuniformity of the atomic density in the APT reconstruction itself (e.g., crystallographic poles and other field evaporation artifacts). This clustering method relies heavily on multiple input parameters, but ideal selection of those parameters is challenging and oftentimes ambiguous. In this study, we utilize a well-known cluster analysis algorithm, called ordering points to identify the clustering structures, and an automatic cluster extraction algorithm to analyze clusters of varying atomic density in APT data. This approach requires only one free parameter, and other inputs can be estimated or bounded based on physical parameters, such as the lattice parameter and solute concentration. The effectiveness of this method is demonstrated by application to several small-scale model datasets and a real APT dataset obtained from an oxide-dispersion strengthened ferritic alloy specimen.

Keywords

atom probe tomographycluster analysisdensity-based clusteringoxide-dispersion strengthened alloy
Type
Data Analysis
Information
Microscopy and Microanalysis , Volume 25 , Special Issue 2: Atom Probe Tomography and Microscopy APT&M 2018 , April 2019 , pp. 338 - 348
Copyright
Copyright © Microscopy Society of America 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ankerst, M, Breunig, MM, Kriegel, H-PSander, J (1999) OPTICS: Ordering points to identify the clustering structure, Davidson, S, Faloutsos, C (Eds) In ACM Sigmod Record, pp. 49–60. New York, NY: ACM.Google Scholar
Bachhav, M, Robert Odette, GMarquis, EA (2014) α′ Precipitation in neutron-irradiated Fe–Cr alloys. Scripta Mater 74, 48–51.Google Scholar
Bailey, NA, Stergar, E, Toloczko, MHosemann, P (2015) Atom probe tomography analysis of high dose MA957 at selected irradiation temperatures. J Nucl Mater 459, 225–234.Google Scholar
Bas, P, Bostel, A, Deconihout, BBlavette, D (1995) A general protocol for the reconstruction of 3D atom probe data. Appl Surf Sci 87, 298–304.Google Scholar
Certain, A, Kuchibhatla, S, Shutthanandan, V, Hoelzer, DAllen, T (2013) Radiation stability of nanoclusters in nano-structured oxide dispersion strengthened (ODS) steels. J Nucl Mater 434(1), 311–321.Google Scholar
Chen, T, Aydogan, E, Gigax, JG, Chen, D, Wang, J, Wang, X, Ukai, S, Garner, FShao, L (2015) Microstructural changes and void swelling of a 12Cr ODS ferritic-martensitic alloy after high-dpa self-ion irradiation. J Nucl Mater 467, 42–49.Google Scholar
Deng, Z, Hu, Y, Zhu, M, Huang, XDu, B (2015) A scalable and fast OPTICS for clustering trajectory big data. Cluster Comput 18(2), 549–562.Google Scholar
Dodge, Y (2008) Kolmogorov–Smirnov Test. New York, NY: Springer New York.Google Scholar
Ester, M, Kriegel, H-P, Sander, JXu, X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise, Simoudis, E, Han, J and Fayyad, U (Eds). In Kdd, pp. 226–231. Palo Alto, CA: AAAI Press.Google Scholar
Felfer, P, Ceguerra, A, Ringer, SCairney, J (2015) Detecting and extracting clusters in atom probe data: A simple, automated method using Voronoi cells. Ultramicroscopy 150, 30–36.Google Scholar
Gault, B, Moody, MP, Cairney, JMRinger, SP (2012) Atom Probe Microscopy. New York, NY: Springer Science & Business Media.Google Scholar
Geiser, B, Larson, D, Oltman, E, Gerstl, S, Reinhard, D, Kelly, TProsa, T (2009) Wide-field-of-view atom probe reconstruction. Microsc Microanal 15(S2), 292–293.Google Scholar
Geiser, BP, Kelly, TF, Larson, DJ, Schneir, JRoberts, JP (2007) Spatial distribution maps for atom probe tomography. Microsc Microanal 13(6), 437–447.Google Scholar
Hartigan, JAHartigan, J (1975) Clustering Algorithms. New York, NY: Wiley.Google Scholar
He, J, Wan, F, Sridharan, K, Allen, TR, Certain, A, Shutthanandan, VWu, Y (2014) Stability of nanoclusters in 14YWT oxide dispersion strengthened steel under heavy ion-irradiation by atom probe tomography. J Nucl Mater 455(1), 41–45.Google Scholar
Hellman, OC, du Rivage, JBSeidman, DN (2003) Efficient sampling for three-dimensional atom probe microscopy data. Ultramicroscopy 95, 199–205.Google Scholar
Hellman, OCSeidman, DN (2002) Measurement of the Gibbsian interfacial excess of solute at an interface of arbitrary geometry using three-dimensional atom probe microscopy. Mater Sci Eng A 327(1), 24–28.Google Scholar
Hellman, OC, Vandenbroucke, JA, Rüsing, J, Isheim, DSeidman, DN (2000) Analysis of three-dimensional atom-probe data by the proximity histogram. Microsc Microanal 6(05), 437–444.Google Scholar
Hubert, LArabie, P (1985) Comparing partitions. J Classification 2(1), 193–218.Google Scholar
Hyde, JMEnglish, CA (2000) An analysis of the structure of irradiation induced Cu-enriched clusters in low and high nickel welds. MRS Online Proc Library Arch 650, R6.6. doi: 0.1557/PROC-650-R6.6.Google Scholar
Jägle, EA, Choi, P-PRaabe, D (2014) The maximum separation cluster analysis algorithm for atom-probe tomography: Parameter determination and accuracy. Microsc Microanal 20(06), 1662–1671.Google Scholar
Lefebvre, W, Philippe, TVurpillot, F (2011) Application of Delaunay tessellation for the characterization of solute-rich clusters in atom probe tomography. Ultramicroscopy 111(3), 200–206.Google Scholar
Mao, Z, Sudbrack, CK, Yoon, KE, Martin, GSeidman, DN (2007) The mechanism of morphogenesis in a phase-separating concentrated multicomponent alloy. Nat Mater 6(3), 210–216.Google Scholar
Marquis, EA, Araullo-Peters, V, Dong, Y, Etienne, A, Fedotova, S, Fujii, K, Fukuya, K, Kuleshova, E, Lopez, A, London, A, Lozano-Perez, S, Nagai, Y, Nishida, K, Radiguet, B, Schreiber, D, Soneda, N, Thuvander, M, Toyama, T, Sefta, FChou, P (2018) On the use of density-based algorithms for the analysis of solute clustering in atom probe tomography data. In Proceedings of the 18th International Conference on Environmental Degradation of Materials in Nuclear Power Systems – Water Reactors: Volume 2, Jackson, JH, Paraventi, D and Wright, M (Eds), August 13–17, 2017, pp. 881–897, Portland, OR: Cham: Springer International Publishing.Google Scholar
Marquis, EAHyde, JM (2010) Applications of atom-probe tomography to the characterisation of solute behaviours. Mater Sci Eng R Rep 69(4), 37–62.Google Scholar
Marquis, EAVurpillot, F (2008) Chromatic aberrations in the field evaporation behavior of small precipitates. Microsc Microanal 14(06), 561–570.Google Scholar
Maurus, SPlant, C (2016) Skinny-dip: Clustering in a sea of noise, Krishnapuram, B and Shah, M (Eds), In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 13–17, 2016, pp. 1055–1064, San Francisco, CA: ACM.Google Scholar
Meilă, M (2003) Comparing clusterings by the variation of information. In Learning Theory and Kernel Machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24–27, 2003, Schölkopf, B and Warmuth, MK (Eds), pp. 173–187. Berlin, Heidelberg: Springer Berlin Heidelberg.Google Scholar
Miller, MKenik, E (2004) Atom probe tomography: A technique for nanoscale characterization. Microsc Microanal 10(03), 336–341.Google Scholar
Patwary, A, Mostofa, M, Palsetia, D, Agrawal, A, Liao, W-K, Manne, FChoudhary, A (2013) Scalable parallel optics data clustering using graph algorithmic techniques, Gropp, W (Ed.), In SC ’13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 17–22 Nov, 2013, pp. 1–12, Denver, CO. New York, NY: ACM.Google Scholar
Ribis, JLozano-Perez, S (2014) Nano-cluster stability following neutron irradiation in MA957 oxide dispersion strengthened material. J Nucl Mater 444(1), 314–322.Google Scholar
Sander, J, Qin, X, Lu, Z, Niu, NKovarsky, A (2003) Automatic extraction of clusters from hierarchical clustering representations, Wang, K, Jeon, J, Shim, K, Srivastava, J (Eds), In Advances in Knowledge Discovery and Data Mining, pp. 75–87. Berilin, Heidelberg: Springer.Google Scholar
Stephenson, LT, Moody, MP, Liddicoat, PVRinger, SP (2007) New techniques for the analysis of fine-scaled clustering phenomena within atom probe tomography (APT) data. Microsc Microanal 13(06), 448–463.Google Scholar
Vaumousse, D, Cerezo, AWarren, P (2003) A procedure for quantification of precipitate microstructures from three-dimensional atom probe data. Ultramicroscopy 95, 215–221.Google Scholar
Vinh, NX, Epps, JBailey, J (2009) Information theoretic measures for clusterings comparison: Is a correction for chance necessary?, Danyluk, A (Ed.), In Proceedings of the 26th Annual International Conference on Machine Learning, June 14–18, 2009, pp. 1073–1080, Montreal, QC, Canada. New York, NY: ACM.Google Scholar
Wharry, JP, Swenson, MJYano, KH (2017) A review of the irradiation evolution of dispersed oxide nanoparticles in the b.c.c. Fe–Cr system: Current understanding and future directions. J Nucl Mater 486, 11–20.Google Scholar
Williams, CA, Haley, D, Marquis, EA, Smith, GDMoody, MP (2013) Defining clusters in APT reconstructions of ODS steels. Ultramicroscopy 132, 271–278.Google Scholar
Zelenty, J, Dahl, A, Hyde, J, Smith, GDMoody, MP (2017) Detecting clusters in atom probe data with Gaussian mixture models. Microsc Microanal 23(2), 269–278.Google Scholar