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

Journal Article · · APL Materials
DOI:https://doi.org/10.1063/1.4950995· OSTI ID:1254517
 [1];  [2];  [3]
  1. Department of Chemical Engineering, University of South Carolina, Columbia, South Carolina 29208, USA, SmartState™ Center for Economic Excellence in the Strategic Approaches to the Generation of Electricity, Columbia, South Carolina 29208, USA
  2. Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, California 91125, USA
  3. Materials Measurement Science Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA, Materials Science and Engineering Department, University of Maryland, College Park, Maryland 20742, USA
+ Show Author Affiliations

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. Here, 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.

Research Organization:
Univ. of South Carolina, Columbia, SC (United States)
Sponsoring Organization:
USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Office of Science (SC)
Grant/Contract Number:
SC0004993; AR0000492
OSTI ID:
1254517
Alternate ID(s):
OSTI ID: 1287441
Journal Information:
APL Materials, Journal Name: APL Materials Vol. 4 Journal Issue: 5; ISSN 2166-532X
Publisher:
American Institute of PhysicsCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 77 works
Citation information provided by
Web of Science

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Cited By (15)

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Tracking materials science data lineage to manage millions of materials experiments and analyses journal July 2019
A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns journal January 2020
Analyzing machine learning models to accelerate generation of fundamental materials insights journal March 2019
CRYSTAL: a multi-agent AI system for automated mapping of materials' crystal structures journal April 2019
Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics journal July 2019
Machine learning in materials informatics: recent applications and prospects journal December 2017
Artificial intelligence for materials discovery journal July 2019
Reconstructing phase diagrams from local measurements via Gaussian processes: mapping the temperature-composition space to confidence journal April 2018
Nanoinformatics, and the big challenges for the science of small things journal January 2019
Classification of MAOX phases and semiconductor screening for next-generation energy conversion ceramic materials journal January 2019
Synthesis, optical imaging, and absorption spectroscopy data for 179072 metal oxides journal March 2019
Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies journal March 2017
Multi-component background learning automates signal detection for spectroscopic data journal July 2019
Understanding and designing magnetoelectric heterostructures guided by computation: progresses, remaining questions, and perspectives journal May 2017

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36 MATERIALS SCIENCE
genetic algorithm
neural-network
combinatorial
optimization
discovery
catalysts
search
X-ray diffraction
phase diagrams
cluster analysis
crystal structure
machine learning