Accelerating materials science with high-throughput computations and machine learning
Abstract
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. In this report 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.
- Authors:
-
- University of California San Diego, La Jolla, CA (United States). Materials Virtual Laboratory
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California San Diego, La Jolla, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF); US Department of the Navy, Office of Naval Research (ONR)
- OSTI Identifier:
- 1528949
- Alternate Identifier(s):
- OSTI ID: 1529421
- Grant/Contract Number:
- SC0012583; SC0012118; 1436976; N00014-15-1-0030; 1411192; N00014-16-1-2621; 1640899
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- Computational Materials Science
- Additional Journal Information:
- Journal Volume: 161; Journal Issue: C; Journal ID: ISSN 0927-0256
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; machine learning; high-throughput; materials discovery; materials design; multi-scale models
Citation Formats
Ong, Shyue Ping. Accelerating materials science with high-throughput computations and machine learning. United States: N. p., 2019.
Web. doi:10.1016/j.commatsci.2019.01.013.
Ong, Shyue Ping. Accelerating materials science with high-throughput computations and machine learning. United States. https://doi.org/10.1016/j.commatsci.2019.01.013
Ong, Shyue Ping. 2019.
"Accelerating materials science with high-throughput computations and machine learning". United States. https://doi.org/10.1016/j.commatsci.2019.01.013. https://www.osti.gov/servlets/purl/1528949.
@article{osti_1528949,
title = {Accelerating materials science with high-throughput computations and machine learning},
author = {Ong, Shyue Ping},
abstractNote = {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. In this report 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.},
doi = {10.1016/j.commatsci.2019.01.013},
url = {https://www.osti.gov/biblio/1528949},
journal = {Computational Materials Science},
issn = {0927-0256},
number = C,
volume = 161,
place = {United States},
year = {Fri Feb 01 00:00:00 EST 2019},
month = {Fri Feb 01 00:00:00 EST 2019}
}
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