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Title: Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge

Abstract

With their ability to rapidly elucidate composition-structure-property relationships, high-throughput experimental studies have revolutionized how materials are discovered, optimized, and commercialized. It is now possible to synthesize and characterize high-throughput libraries that systematically address thousands of individual cuts of fabrication parameter space. An unresolved issue remains transforming structural characterization data into phase mappings. This difficulty is related to the complex information present in diffraction and spectroscopic data and its variation with composition and processing. Here, we review the field of automated phase diagram attribution and discuss the impact that emerging computational approaches will have in the generation of phase diagrams and beyond.

Authors:
 [1];  [2];  [3]
  1. Department of Chemical Engineering, University of South Carolina, Columbia, South Carolina 29208, USA, SmartState™ Center for Economic Excellence in the Strategic Approaches to the Generation of Electricity, Columbia, South Carolina 29208, USA
  2. Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, California 91125, USA
  3. Materials Measurement Science Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA, Materials Science and Engineering Department, University of Maryland, College Park, Maryland 20742, USA
Publication Date:
Research Org.:
Univ. of South Carolina, Columbia, SC (United States)
Sponsoring Org.:
USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Office of Science (SC)
OSTI Identifier:
1254517
Alternate Identifier(s):
OSTI ID: 1287441
Grant/Contract Number:  
SC0004993; AR0000492
Resource Type:
Published Article
Journal Name:
APL Materials
Additional Journal Information:
Journal Name: APL Materials Journal Volume: 4 Journal Issue: 5; Journal ID: ISSN 2166-532X
Publisher:
American Institute of Physics
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; genetic algorithm; neural-network; combinatorial; optimization; discovery; catalysts; search; X-ray diffraction; phase diagrams; cluster analysis; crystal structure; machine learning

Citation Formats

Hattrick-Simpers, Jason R., Gregoire, John M., and Kusne, A. Gilad. Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge. United States: N. p., 2016. Web. doi:10.1063/1.4950995.
Hattrick-Simpers, Jason R., Gregoire, John M., & Kusne, A. Gilad. Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge. United States. doi:10.1063/1.4950995.
Hattrick-Simpers, Jason R., Gregoire, John M., and Kusne, A. Gilad. Thu . "Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge". United States. doi:10.1063/1.4950995.
@article{osti_1254517,
title = {Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge},
author = {Hattrick-Simpers, Jason R. and Gregoire, John M. and Kusne, A. Gilad},
abstractNote = {With their ability to rapidly elucidate composition-structure-property relationships, high-throughput experimental studies have revolutionized how materials are discovered, optimized, and commercialized. It is now possible to synthesize and characterize high-throughput libraries that systematically address thousands of individual cuts of fabrication parameter space. An unresolved issue remains transforming structural characterization data into phase mappings. This difficulty is related to the complex information present in diffraction and spectroscopic data and its variation with composition and processing. Here, we review the field of automated phase diagram attribution and discuss the impact that emerging computational approaches will have in the generation of phase diagrams and beyond.},
doi = {10.1063/1.4950995},
journal = {APL Materials},
number = 5,
volume = 4,
place = {United States},
year = {2016},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1063/1.4950995

Citation Metrics:
Cited by: 7 works
Citation information provided by
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    Works referencing / citing this record:

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