Accelerating materials science with high-throughput computations and machine learning

[Display omitted] With unprecedented amounts of materials data generated from experiments as well as high-throughput density functional theory calculations, machine learning techniques has the potential to greatly accelerate materials discovery and design. Here, we review our efforts in the Material...

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Bibliographic Details
Published inComputational materials science Vol. 161; no. C; pp. 143 - 150
Main Author Ong, Shyue Ping
Format Journal Article
LanguageEnglish
Published United States Elsevier B.V 15.04.2019
Elsevier
Subjects
High-throughput
Machine learning
Materials design
Materials discovery
MATERIALS SCIENCE
Multi-scale models
High-throughput
Materials discovery
Materials design
Machine learning
Multi-scale models
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Summary:[Display omitted] With unprecedented amounts of materials data generated from experiments as well as high-throughput density functional theory calculations, machine learning techniques has the potential to greatly accelerate materials discovery and design. Here, we review our efforts in the Materials Virtual Lab to integrate software automation, data generation and curation and machine learning to (i) design and optimize technological materials for energy storage, energy efficiency and high-temperature alloys; (ii) develop scalable quantum-accurate models, and (iii) enhance the speed and accuracy in interpreting characterization spectra.
Bibliography:National Science Foundation (NSF)
USDOE Office of Science (SC), Basic Energy Sciences (BES)
US Department of the Navy, Office of Naval Research (ONR)
SC0012583; SC0012118; 1436976; N00014-15-1-0030; 1411192; N00014-16-1-2621; 1640899
ISSN:0927-0256
1879-0801
DOI:10.1016/j.commatsci.2019.01.013