English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

MPS-Authors
/persons/resource/persons181390

Kühbach,  Markus Tobias
Theory and Simulation, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

/persons/resource/persons225499

Bajaj,  Priyanshu
Alloys for Additive Manufacturing, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
m4p material solutions GmbH, Magdeburg, Germany;

/persons/resource/persons212741

Zhao,  Huan
Mechanism-based Alloy Design, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

/persons/resource/persons75620

Jägle,  Eric Aimé
Alloys for Additive Manufacturing, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
Institute of Materials Science, Universität der Bundeswehr München, Neubiberg, Germany;

/persons/resource/persons185411

Gault,  Baptiste
Atom Probe Tomography, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
Imperial College, Royal School of Mines, Department of Materials, London, SW7 2AZ, UK;

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
Citation

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.


Cite as: http://hdl.handle.net/21.11116/0000-0007-F0F7-3
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).