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

Jason HattrickSimpers | Zendy; John M. Gregoire | Zendy; A. Gilad Kusne | Zendy

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

Author(s) -

Jason HattrickSimpers,

John M. Gregoire,

A. Gilad Kusne

Publication year - 2016

Publication title -

apl materials

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.571

H-Index - 60

ISSN - 2166-532X

DOI - 10.1063/1.4950995

Subject(s) - throughput , property (philosophy) , perspective (graphical) , materials science , characterization (materials science) , computer science , field (mathematics) , phase diagram , phase (matter) , nanotechnology , artificial intelligence , physics , telecommunications , philosophy , mathematics , epistemology , pure mathematics , wireless , quantum mechanics

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. 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

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