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

Published

Author(s)

Aaron G. Kusne, Jason Hattrick-Simpers, John Gregoire

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.
Citation
Applied Physics Letters Materials

Keywords

MGI, Combinatorial libraries, phase diagram, machine learning

Citation

Kusne, A. , Hattrick-Simpers, J. and Gregoire, J. (2016), Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge, Applied Physics Letters Materials, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=920361 (Accessed December 6, 2021)
Created May 26, 2016, Updated June 3, 2017