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

Published online by Cambridge University Press:  30 August 2017

Fang Ren ,
Travis Williams ,
Jason Hattrick-Simpers  and
Apurva Mehta
Fang Ren*
Affiliation:
Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
Travis Williams
Affiliation:
College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA
Jason Hattrick-Simpers
Affiliation:
Materials for Energy and Sustainable Development Group, National Institute of Standards and Technology, MD 20899, USA
Apurva Mehta*
Affiliation:
Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
*
Address all correspondence to Apurva Mehta, Fang Ren at mehta@slac.stanford.edu, fangren@slac.stanford.edu.
Address all correspondence to Apurva Mehta, Fang Ren at mehta@slac.stanford.edu, fangren@slac.stanford.edu.
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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.

Type
Research Letters
Information
MRS Communications , Volume 7 , Issue 3 , September 2017 , pp. 613 - 620
Copyright
Copyright © Materials Research Society 2017 

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Footnotes

†

These authors contributed equally to this work.

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