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

npj Computational Materials 2021年第1期7卷 148-157页

作者： markus kühbach Priyanshu Bajaj Huan Zhao Murat HÇelik Eric AJägle Baptiste Gault Max-Planck-Institut für Eisenforschung GmbH(MPIE) DüsseldorfGermany m4p material solutions GmbH MagdeburgGermany Institute for Advanced Simulation(IAS) Jülich Supercomputing Centre(JSC)JülichGermany Universität der Bundeswehr München NeubibergGermany Department of Materials Imperial College LondonRoyal School of MinesLondonUK

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 *** such tools are mature already in the condensed-matter physics community,the situation is still very different for many *** probe tomography(APT)is one *** microscopy and microanalysis technique has matured into a versatile nano-analytical characterization tool with applications that range from materials science to geology and possibly ***,data science tools are required for extracting chemo-structural spatial correlations from the reconstructed point *** 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 *** 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 *** is particularly the case for open-source tools that support scientific computing hardware,tools which enable high-throughput workflows,and open welldocumented metadata standards to align experimental research better with the fair data stewardship *** this end,we introduce paraprobe,an open-source tool for scientific computing and high-throughput studying of point cloud data,here exemplified with *** 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 *** tools work well in concert with Python and HDF5 to enable several orders of magnitude performance gain,automation,and reproducibility.

关键词： hardware Python execution

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