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

Hattrick-Simpers, Jason R. and Gregoire, John M. and Kusne, A. Gilad (2016) Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge. APL Materials, 4 (5). Art. No. 053211. ISSN 2166-532X. https://resolver.caltech.edu/CaltechAUTHORS:20160616-071550684

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1063/1.4950995DOIArticle
http://scitation.aip.org/content/aip/journal/aplmater/4/5/10.1063/1.4950995PublisherArticle
ORCID:
AuthorORCID
Gregoire, John M.0000-0002-2863-5265
Additional Information:© 2016 Author(s). © 2016 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Received 15 February 2016; accepted 9 May 2016; published online 26 May 2016. J.H.S gratefully acknowledges support from Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award No. DE-AR0000492 and the SouthCarolina SmartState™ Center for Strategic Approaches to the Generation of Electricity (SAGE). J.M.G. acknowledges support from the Joint Center for Artificial Photosynthesis, a DOE Energy Innovation Hub, supported through the Office of Science of the U.S. Department of Energy Award No. DE-SC0004993, and the Computational Sustainability Network, supported through National Science Foundation Expeditions in Computing Grant No. 1521687. The authors thank Carla Gomes and Ronan LeBras for insightful discussions.
Group:JCAP
Funders:
Funding AgencyGrant Number
ARPA-EUNSPECIFIED
Department of Energy (DOE)DE-AR0000492
South Carolina Smart-State Center for Strategic Approaches to the Generation of Electricity (SAGE)UNSPECIFIED
Department of Energy (DOE)DE-SC0004993
NSFCCF-1521687
Issue or Number:5
Record Number:CaltechAUTHORS:20160616-071550684
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160616-071550684
Official Citation:Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge Hattrick-Simpers, Jason R. and Gregoire, John M. and Kusne, A. Gilad, APL Mater., 4, 053211 (2016), DOI:http://dx.doi.org/10.1063/1.4950995
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:67957
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:16 Jun 2016 20:16
Last Modified:03 Oct 2019 10:10

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