Abstract
Density functional theory (DFT) has been widely applied in modern materials discovery and many materials databases, including the open quantum materials database (OQMD), contain large collections of calculated DFT properties of experimentally known crystal structures and hypothetical predicted compounds. Since the beginning of the OQMD in late 2010, over one million compounds have now been calculated and stored in the database, which is constantly used by worldwide researchers in advancing materials studies. The growth of the OQMD depends on project-based high-throughput DFT calculations, including structure-based projects, property-based projects, and most recently, machine-learning-based projects. Another major goal of the OQMD is to ensure the openness of its materials data to the public and the OQMD developers are constantly working with other materials databases to reach a universal querying protocol in support of the FAIR data principles.
| Original language | English (US) |
|---|---|
| Article number | 031001 |
| Journal | JPhys Materials |
| Volume | 5 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jul 1 2022 |
Funding
The authors acknowledge support from the Award 70NANB19H0005 from the U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD), from the Materials Research Science and Engineering Centers (MRSEC) program (NSF Grant No. DMR-1720319) at the Materials Research Center of Northwestern University and from the U.S. Department of Energy, through the Office of Energy Efficiency and Renewable Energy (EERE) Contract DE-EE0008089. M A is currently employed at Rivian Automotive in Palo Alto, California.
Keywords
- high-throughput DFT
- machine learning
- materials database
- materials discovery
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics
- General Materials Science
- Condensed Matter Physics