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Title: On-the-fly data assessment for high-throughput x-ray diffraction measurements

Investment in brighter sources and larger and faster detectors has accelerated the speed of data acquisition at national user facilities. The accelerated data acquisition offers many opportunities for the discovery of new materials, but it also presents a daunting challenge. The rate of data acquisition far exceeds the current speed of data quality assessment, resulting in less than optimal data and data coverage, which in extreme cases forces recollection of data. Herein, we show how this challenge can be addressed through the development of an approach that makes routine data assessment automatic and instantaneous. By extracting and visualizing customized attributes in real time, data quality and coverage, as well as other scientifically relevant information contained in large data sets, is highlighted. Deployment of such an approach not only improves the quality of data but also helps optimize the usage of expensive characterization resources by prioritizing measurements of the highest scientific impact. We anticipate our approach will become a starting point for a sophisticated decision-tree that optimizes data quality and maximizes scientific content in real time through automation. Finally, with these efforts to integrate more automation in data collection and analysis, we can truly take advantage of the accelerating speed ofmore » data acquisition.« less
Authors:
 [1] ;  [2] ;  [1] ;  [2] ; ORCiD logo [1]
  1. SLAC National Accelerator Lab., Menlo Park, CA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Grant/Contract Number:
AC02-76SF00515; AC02-05CH11231
Type:
Accepted Manuscript
Journal Name:
ACS Combinatorial Science
Additional Journal Information:
Journal Volume: 19; Journal Issue: 6; Journal ID: ISSN 2156-8952
Publisher:
American Chemical Society (ACS)
Research Org:
SLAC National Accelerator Lab., Menlo Park, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; attribute extraction; data quality assessment; high-throughput; on-the-fly; X-ray diffraction
OSTI Identifier:
1369408
Alternate Identifier(s):
OSTI ID: 1458497

Ren, Fang, Pandolfi, Ronald, Van Campen, Douglas, Hexemer, Alexander, and Mehta, Apurva. On-the-fly data assessment for high-throughput x-ray diffraction measurements. United States: N. p., Web. doi:10.1021/acscombsci.7b00015.
Ren, Fang, Pandolfi, Ronald, Van Campen, Douglas, Hexemer, Alexander, & Mehta, Apurva. On-the-fly data assessment for high-throughput x-ray diffraction measurements. United States. doi:10.1021/acscombsci.7b00015.
Ren, Fang, Pandolfi, Ronald, Van Campen, Douglas, Hexemer, Alexander, and Mehta, Apurva. 2017. "On-the-fly data assessment for high-throughput x-ray diffraction measurements". United States. doi:10.1021/acscombsci.7b00015. https://www.osti.gov/servlets/purl/1369408.
@article{osti_1369408,
title = {On-the-fly data assessment for high-throughput x-ray diffraction measurements},
author = {Ren, Fang and Pandolfi, Ronald and Van Campen, Douglas and Hexemer, Alexander and Mehta, Apurva},
abstractNote = {Investment in brighter sources and larger and faster detectors has accelerated the speed of data acquisition at national user facilities. The accelerated data acquisition offers many opportunities for the discovery of new materials, but it also presents a daunting challenge. The rate of data acquisition far exceeds the current speed of data quality assessment, resulting in less than optimal data and data coverage, which in extreme cases forces recollection of data. Herein, we show how this challenge can be addressed through the development of an approach that makes routine data assessment automatic and instantaneous. By extracting and visualizing customized attributes in real time, data quality and coverage, as well as other scientifically relevant information contained in large data sets, is highlighted. Deployment of such an approach not only improves the quality of data but also helps optimize the usage of expensive characterization resources by prioritizing measurements of the highest scientific impact. We anticipate our approach will become a starting point for a sophisticated decision-tree that optimizes data quality and maximizes scientific content in real time through automation. Finally, with these efforts to integrate more automation in data collection and analysis, we can truly take advantage of the accelerating speed of data acquisition.},
doi = {10.1021/acscombsci.7b00015},
journal = {ACS Combinatorial Science},
number = 6,
volume = 19,
place = {United States},
year = {2017},
month = {5}
}