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/content/aip/journal/aplmater/4/5/10.1063/1.4950995
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See supplementary material at http://dx.doi.org/10.1063/1.4950995 for the AutoPhase workflow diagram in Figure S1 and the GRENDEL workflow diagram in Figure S2.[Supplementary Material]
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Certain commercial equipment, instruments, or materials are identified in this report in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.
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/content/aip/journal/aplmater/4/5/10.1063/1.4950995
2016-05-26
2016-12-06

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. 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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