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

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. Univ. of South Carolina, Columbia, SC (United States); Center for Economic Excellence in the Strategic Approaches to the Generation of Electricity, Columbia, SC (United States)
  2. California Institute of Technology, Pasadena, CA (United States)
  3. National Inst. of Standards and Technology, Gaithersburg, MD (United States); Univ. of Maryland, College Park, MD (United States)
Publication Date:
OSTI Identifier:
1254517
Grant/Contract Number:
AR0000492; SC0004993
Type:
Published Article
Journal Name:
APL Materials
Additional Journal Information:
Journal Volume: 4; Journal Issue: 5; Journal ID: ISSN 2166-532X
Publisher:
American Institute of Physics (AIP)
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)
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