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This content will become publicly available on August 30, 2018

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

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. As a result, on-the-fly segmentation would, therefore, result in accelerated scientific productivity.
Authors:
 [1] ;  [2] ;  [3] ;  [1]
  1. SLAC National Accelerator Lab., Menlo Park, CA (United States)
  2. Univ. of South Carolina, Columbia, SC (United States)
  3. National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States)
Publication Date:
Grant/Contract Number:
AC02-76SF00515
Type:
Accepted Manuscript
Journal Name:
MRS Communications
Additional Journal Information:
Journal Volume: 7; Journal Issue: 03; Journal ID: ISSN 2159-6859
Publisher:
Materials Research Society - Cambridge University Press
Research Org:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; X-ray diffraction (XRD); amorphous metal
OSTI Identifier:
1394638