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Title: Big–deep–smart data in imaging for guiding materials design

Journal Article · · Nature Materials
DOI:https://doi.org/10.1038/NMAT4395· OSTI ID:1393877
 [1];  [2];  [3]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Inst. for Functional Imaging of Materials and Center for Nanophase Materials Sciences
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Inst. for Functional Imaging of Materials, Center for Nanophase Materials Sciences, and Computer Science and Mathematics Division
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Inst. for Functional Imaging of Materials and Computer Science and Mathematics Division
+ Show Author Affiliations

Harnessing big data, deep data, and smart data from state-of-the-art imaging might accelerate the design and realization of advanced functional materials. Here we discuss new opportunities in materials design enabled by the availability of big data in imaging and data analytics approaches, including their limitations, in material systems of practical interest. We specifically focus on how these tools might help realize new discoveries in a timely manner. Such methodologies are particularly appropriate to explore in light of continued improvements in atomistic imaging, modelling and data analytics methods.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Organization:
DOE Office of Science (SC)
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1393877
Journal Information:
Nature Materials, Vol. 14, Issue 10; ISSN 1476-1122
Publisher:
Springer Nature - Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 235 works
Citation information provided by
Web of Science

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  • Marzouk, Youssef M.
  • BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Conference Proceedings https://doi.org/10.1063/1.2149785
conference January 2005
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A penalized autologistic regression with application for modeling the microstructure of dual-phase high-strength steel journal June 2019
Probing electromechanical behaviors by datacube piezoresponse force microscopy in ambient and aqueous environments journal March 2019
Surface-screening mechanisms in ferroelectric thin films and their effect on polarization dynamics and domain structures journal January 2018
Layer dependence of graphene-diamene phase transition in epitaxial and exfoliated few-layer graphene using machine learning journal June 2019
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Machine Learning Topological States text January 2016
Deep Learning for Computational Chemistry preprint January 2017
Matrix Product Operators for Sequence to Sequence Learning text January 2018
Artificial Intelligent Atomic Force Microscope Enabled by Machine Learning text January 2018
Mapping Intrinsic Electromechanical Responses at the Nanoscale via Sequential Excitation Scanning Probe Microscopy Empowered by Deep Data text January 2018
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