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

Published: May 26, 2016

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
Pub Type: Journals

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Keywords

MGI, Combinatorial libraries, phase diagram, machine learning
Created May 26, 2016, Updated June 03, 2017