skip to main content

Explore Research Products in the NSF-PAR

Title: Density functional theory-based electric field gradient database
Abstract 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/ ).  more » « less
Award ID(s):
1711118
NSF-PAR ID:
10304086
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Scientific Data
Volume:
7
Issue:
1
ISSN:
2052-4463
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ; ; ; et al ( , npj Computational Materials)
    null (Ed.)
    Abstract The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov . 
    more » « less
  2. ; ; ; ; ; ; ; ; ( , npj Computational Materials)
    Abstract

    Computational materials discovery efforts are enabled by large databases of properties derived from high-throughput density functional theory (DFT), which now contain millions of calculations at the generalized gradient approximation (GGA) level of theory. It is now feasible to carry out high-throughput calculations using more accurate methods, such as meta-GGA DFT; however recomputing an entire database with a higher-fidelity method would not effectively leverage the enormous investment of computational resources embodied in existing (GGA) calculations. Instead, we propose here a general procedure by which higher-fidelity, low-coverage calculations (e.g., meta-GGA calculations for selected chemical systems) can be combined with lower-fidelity, high-coverage calculations (e.g., an existing database of GGA calculations) in a robust and scalable manner. We then use legacy PBE(+U) GGA calculations and new r2SCAN meta-GGA calculations from the Materials Project database to demonstrate that our scheme improves solid and aqueous phase stability predictions, and discuss practical considerations for its implementation.

     
    more » « less
  3. ; ; ( , Scientific Data)
    Abstract

    Perovskite oxides (ternary chemical formula ABO3) are a diverse class of materials with applications including heterogeneous catalysis, solid-oxide fuel cells, thermochemical conversion, and oxygen transport membranes. However, their multicomponent (chemical formula$${A}_{x}{A}_{1-x}^{\text{'}}{B}_{y}{B}_{1-y}^{\text{'}}{O}_{3}$$AxA1−x'ByB1−y'O3) chemical space is underexplored due to the immense number of possible compositions. To expand the number of computed$${A}_{x}{A}_{1-x}^{{\prime} }{B}_{y}{B}_{1-y}^{{\prime} }{O}_{3}$$AxA1−x′ByB1−y′O3compounds we report a dataset of 66,516 theoretical multinary oxides, 59,708 of which are perovskites. First, 69,407$${A}_{0.5}{A}_{0.5}^{{\prime} }{B}_{0.5}{B}_{0.5}^{{\prime} }{O}_{3}$$A0.5A0.5′B0.5B0.5′O3compositions were generated in thea−b+a−Glazer tilting mode using the computationally-inexpensive Structure Prediction and Diagnostic Software (SPuDS) program. Next, we optimized these structures with density functional theory (DFT) using parameters compatible with the Materials Project (MP) database. Our dataset contains these optimized structures and their formation (ΔHf) and decomposition enthalpies (ΔHd) computed relative to MP tabulated elemental references and competing phases, respectively. This dataset can be mined, used to train machine learning models, and rapidly and systematically expanded by optimizing more SPuDS-generated$${A}_{0.5}{A}_{0.5}^{{\prime} }{B}_{0.5}{B}_{0.5}^{{\prime} }{O}_{3}$$A0.5A0.5′B0.5B0.5′O3perovskite structures using MP-compatible DFT calculations.

     
    more » « less
  4. ; ; ; ( , Physical Chemistry Chemical Physics)
    CuZrO 3 has been hypothesized to be a catalytic material with potential applications for CO 2 reduction. Unfortunately, this material has received limited attention in the literature, and to the best of our knowledge the exact crystal structure is still unknown. To address this challenge, we utilize several different structural prediction techniques in concert, including the Universal Structure Predictor: Evolutionary Xtallography (USPEX), the Materials Project Structure Predictor, and the Open Quantum Materials Database (OQMD). Leveraging these structural prediction techniques in conjunction with Density-Functional Theory (DFT) calculations, we determine a possible structure for CuZrO 3 , which resembles a “sandwich” morphology. Our calculations reveal that this new structure is significantly lower in energy than a previously hypothesized perovskite structure, albeit it still has a thermodynamic preference to decompose into CuO and ZrO 2 . In addition, we experimentally tried to synthesize CuZrO 3 based on literature reports and compared computational to experimental X-ray Diffraction (XRD) patterns confirming that the final product is a mixture of CuO and ZrO 2 . Finally, we conducted a computational surface energetics and CO 2 adsorption study on our discovered sandwich morphology, demonstrating that CO 2 can adsorb and activate on the material. However, these CO 2 adsorption results deviate from previously reported results further confirming that the CuZrO 3 is a metastable form and may not be experimentally accessible as a well-mixed oxide, since phase segregation to CuO and ZrO 2 is preferred. Taken together, our combined computational and experimental study provides evidence that the synthesis of CuZrO 3 is extremely difficult and if this oxide exists, it should have a sandwich-like morphology. 
    more » « less
  5. ; ; ; ; ; ; ( , npj Computational Materials)
    Abstract

    Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts’ heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystal material design with high structural diversity and symmetry. Our model increases the generation validity by more than 700% compared to FTCP, one of the latest structure generators and by more than 45% compared to our previous CubicGAN model. Density Functional Theory (DFT) calculations are used to validate the generated structures with 1869 materials out of 2000 are successfully optimized and deposited into the Carolina Materials Databasewww.carolinamatdb.org, of which 39.6% have negative formation energy and 5.3% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potential synthesizability.

     
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email