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  On strong-scaling and open-source tools for analyzing atom probe tomography data

Kühbach, M. T., Bajaj, P., Zhao, H., Çelik, M. H., Jägle, E. A., & Gault, B. (2021). On strong-scaling and open-source tools for analyzing atom probe tomography data. npj Computational Materials, 7(1): 21. doi:10.1038/s41524-020-00486-1.

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On strong-scaling and open-source tools for analyzing atom probe tomography data.pdf (Supplementary material), 2MB
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On strong-scaling and open-source tools for analyzing atom probe tomography data.pdf
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 Creators:
Kühbach, Markus Tobias1, Author              
Bajaj, Priyanshu2, 3, Author              
Zhao, Huan4, Author              
Çelik, Murat H.5, Author              
Jägle, Eric Aimé2, 6, Author              
Gault, Baptiste7, 8, Author              
Affiliations:
1Theory and Simulation, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863392              
2Alloys for Additive Manufacturing, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_2117289              
3m4p material solutions GmbH, Magdeburg, Germany, ou_persistent22              
4Mechanism-based Alloy Design, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863383              
5Institute for Advanced Simulation (IAS), Jülich Supercomputing Centre (JSC), Jülich, Germany, ou_persistent22              
6Institute of Materials Science, Universität der Bundeswehr München, Neubiberg, Germany, ou_persistent22              
7Atom Probe Tomography, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863384              
8Imperial College, Royal School of Mines, Department of Materials, London, SW7 2AZ, UK, ou_persistent22              

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Free keywords: Characterization (materials science); Computational geometry; Data Science; Large dataset; Metadata; Open Data; Open systems; Probes; Software design, Analytical characterization; Atom-probe tomography; Computational materials science; Experimental research; High-throughput method; Microscopy and microanalysis techniques; Proprietary software; Scientific community, Open source software
 Abstract: The development of strong-scaling computational tools for high-throughput methods with an open-source code and transparent metadata standards has successfully transformed many computational materials science communities. While such tools are mature already in the condensed-matter physics community, the situation is still very different for many experimentalists. Atom probe tomography (APT) is one example. This microscopy and microanalysis technique has matured into a versatile nano-analytical characterization tool with applications that range from materials science to geology and possibly beyond. Here, data science tools are required for extracting chemo-structural spatial correlations from the reconstructed point cloud. For APT and other high-end analysis techniques, post-processing is mostly executed with proprietary software tools, which are opaque in their execution and have often limited performance. Software development by members of the scientific community has improved the situation but compared to the sophistication in the field of computational materials science several gaps remain. This is particularly the case for open-source tools that support scientific computing hardware, tools which enable high-throughput workflows, and open well-documented metadata standards to align experimental research better with the fair data stewardship principles. To this end, we introduce paraprobe, an open-source tool for scientific computing and high-throughput studying of point cloud data, here exemplified with APT. We show how to quantify uncertainties while applying several computational geometry, spatial statistics, and clustering tasks for post-processing APT datasets as large as two billion ions. These tools work well in concert with Python and HDF5 to enable several orders of magnitude performance gain, automation, and reproducibility. © 2021, The Author(s).

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Language(s): eng - English
 Dates: 2021-12
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41524-020-00486-1
 Degree: -

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Title: npj Computational Materials
  Abbreviation : npj Comput. Mater.
Source Genre: Journal
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Publ. Info: London : Springer Nature
Pages: - Volume / Issue: 7 (1) Sequence Number: 21 Start / End Page: - Identifier: ISSN: 2057-3960
CoNE: https://pure.mpg.de/cone/journals/resource/2057-3960