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On-the-fly segmentation approaches for x-ray diffraction datasets for metallic glasses

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Abstract

Investment in brighter sources and larger detectors has resulted in an explosive rise in the data collected at synchrotron facilities. Currently, human experts extract scientific information from these data, but they cannot keep pace with the rate of data collection. Here, we present three on-the-fly approaches—attribute extraction, nearest-neighbor distance, and cluster analysis—to quickly segment x-ray diffraction (XRD) data into groups with similar XRD profiles. An expert can then analyze representative spectra from each group in detail with much reduced time, but without loss of scientific insights. On-the-fly segmentation would, therefore, result in accelerated scientific productivity.

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Acknowledgement

This work was supported by the Advanced Manufacturing Office of the Department of Energy under FWP-100250. Use of the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515. T. Williams was supported by NSF IGERT Grant #1250052. The authors thank D. Van Campen and T. Dunn for their support when collecting XRD data at SSRL Beamline1-5. The authors would like to thank the South Carolina Center of Economic Excellence for Strategic Approaches to the Generation of Electricity for support in sample synthesis. The authors acknowledge 2016 Machine Learning for Materials Research Workshop sponsored by NIST and University of Maryland for the introduction on unsupervised machine learning. The authors also thank D. Schneider for recommending cluster analysis methods for this work.

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Authors and Affiliations

  1. Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA

    Fang Ren & Apurva Mehta

  2. College of Engineering and Computing, University of South Carolina, Columbia, SC, 29208, USA

    Travis Williams

  3. Materials for Energy and Sustainable Development Group, National Institute of Standards and Technology, MD, 20899, USA

    Jason Hattrick-Simpers

Authors
  1. Fang Ren
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  2. Travis Williams
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  4. Apurva Mehta
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Corresponding authors

Correspondence to Fang Ren or Apurva Mehta.

Additional information

These authors contributed equally to this work.

Supplementary Materials

Supplementary Materials

The supplementary material for this article can be found at https://doi.org/10.1557/mrc.2017.76

All the data are organized database and uploaded to Citrine.io (https://citrination.com/datasets/153238/show_search) and Materials website at NIST (http://hdl.handle.net/11256/945). The source code used to generate all the plots can be downloaded at https://github.com/fang-ren/Unsupervised_data_analysis_CoFeZr.

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Ren, F., Williams, T., Hattrick-Simpers, J. et al. On-the-fly segmentation approaches for x-ray diffraction datasets for metallic glasses. MRS Communications 7, 613–620 (2017). https://doi.org/10.1557/mrc.2017.76

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  • DOI: https://doi.org/10.1557/mrc.2017.76

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