Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
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Computational approaches championed by the Materials Project, OQMD, AFLOWLIB and NOMAD have used density functional theory as approximations of physical energies.
“Instead, computational approaches championed by the Materials Project (MP)16, the Open Quantum Materials Database (OQMD)17, AFLOWLIB20 and NOMAD21 have used first-principles calculations based on density functional theory (DFT) as approximations of physical energies.”
From Scaling deep learning for materials discovery (2023) · cites this paper as [17]
Final GNoME models improve the precision of stable predictions (hit rate) to above 80% with structure and 33% per 100 trials with composition only, compared with 1% in previous work.
“Final GNoME models accurately predict energies to 11 meV atom−1 and improve the precision of stable predictions (hit rate) to above 80% with structure and 33% per 100 trials with composition only, compared with 1% in previous work17.”
From Scaling deep learning for materials discovery (2023) · cites this paper as [17]
Final GNoME models accurately predict energies to 11 meV atom−1.
“Final GNoME models accurately predict energies to 11 meV atom−1 and improve the precision of stable predictions (hit rate) to above 80% with structure and 33% per 100 trials with composition only, compared with 1% in previous work17.”
From Scaling deep learning for materials discovery (2023) · cites this paper as [17]
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- Authors
- Saal, J. E., Kirklin, S., Aykol, M., Meredig, B., & Wolverton, C.
- Affiliations
- Northwestern University (US)
- Corresponding author
- Wolverton, C.
- Venue
- JOM
- Published
- 2013-09-28
- Volume
- 65
- Issue
- 11
- Pages
- 1501-1509
- ISSN
- 0148-6608 (ISSN-L)
- 1047-4838
- 1543-1851
- OpenAlex
- W1976492731
- Cited by
- 2,584
- References
- 4
- Citations by year
- 2026: 342 · 2025: 380 · 2024: 371 · 2023: 276 · 2022: 288 · 2021: 225 · 2020: 192 · 2019: 165 · 2018: 138 · 2017: 84 · 2016: 69 · 2015: 39 · 2014: 13 · 2013: 1
- FWCI
- 16.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- JOM (publishedVersion)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Keywords
- high-throughput density functional theory, materials database, materials discovery, lithium-oxygen batteries, lithium-ion battery anodes, magnesium alloys, crystal structure
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