Density functional theory-based electric field gradient database

• Published in: Scientific data Vol. 7; no. 1; p. 362
• Main Authors: Choudhary, Kamal, Ansari, Jaafar N., Mazin, Igor I., Sauer, Karen L.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21.10.2020; Nature Publishing Group
• Subjects: 639/301/1034/1038; 639/766/387/1129; Computer applications; Data Descriptor; Electric fields; Humanities and Social Sciences; multidisciplinary; Science; Science (multidisciplinary); Spectroscopy; Statistical analysis
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-020-00707-8

The deviation of the electron density around the nuclei from spherical symmetry determines the electric field gradient (EFG), which can be measured by various types of spectroscopy. Nuclear Quadrupole Resonance (NQR) is particularly sensitive to the EFG. The EFGs, and by implication NQR frequencies, vary dramatically across materials. Consequently, searching for NQR spectral lines in previously uninvestigated materials represents a major challenge. Calculated EFGs can significantly aid at the search’s inception. To facilitate this task, we have applied high-throughput density functional theory calculations to predict EFGs for 15187 materials in the JARVIS-DFT database. This database, which will include EFG as a standard entry, is continuously increasing. Given the large scope of the database, it is impractical to verify each calculation. However, we assess accuracy by singling out cases for which reliable experimental information is readily available and compare them to the calculations. We further present a statistical analysis of the results. The database and tools associated with our work are made publicly available by JARVIS-DFT ( https://www.ctcms.nist.gov/~knc6/JVASP.html ) and NIST-JARVIS API ( http://jarvis.nist.gov/ ). Measurement(s) electric field gradient Technology Type(s) computational modeling technique Factor Type(s) material studied Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13027220