Journal
COMPUTATIONAL MATERIALS SCIENCE
Volume 161, Issue -, Pages 143-150Publisher
ELSEVIER
DOI: 10.1016/j.commatsci.2019.01.013
Keywords
Machine learning; High-throughput; Materials discovery; Materials design; Multi-scale models
Categories
Funding
- NorthEast Center for Chemical Energy Storage (NECCES), an Energy Frontier Research Center - U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences [DESC0012583]
- National Science Foundation (NSF) Designing Materials to Revolutionize and Engineer our Future (DMREF) program [1436976]
- U.S. DOE, Office of Science, Basic Energy Sciences [DESC0012118]
- National Security Science and Engineering Faculty Fellowship (NSSEFF) under Office of Naval Research (ONR) [N00014-15-1-0030]
- NSF Ceramics [1411192]
- ONR Young Investigator Program (YIP) [N00014-16-1-2621]
- NSF Data Infrastructure Building Blocks (DIBBS) [1640899]
- National Science Foundation [ACI-1053575]
- Direct For Mathematical & Physical Scien
- Division Of Materials Research [1411192] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1436976] Funding Source: National Science Foundation
Ask authors/readers for more resources
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.
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