GIPAW Pseudopotentials of d Elements for Solid-State NMR
Christian Tantardini1,2, Alexander G Kvashnin3, Davide Ceresoli4
1Department of Chemistry, UiT The Arctic University of Norway, P.O. Box 6050 Langnes, N-9037 Tromsø, Norway.
Computational methods aid in interpreting solid-state nuclear magnetic resonance (NMR) spectra. This study introduces new methods for predicting NMR parameters in inorganic materials, enhancing accuracy and enabling machine learning applications.
Area of Science:
- Solid-state chemistry
- Computational materials science
- Spectroscopy
Background:
- Density functional theory (DFT) offers a balance of efficiency and accuracy for solid-state chemistry.
- DFT aids in assigning NMR spectral signals and identifying spectral anomalies.
- Gauge-including projector augmented wave (GIPAW) methods are effective for organic crystals but less explored for inorganic materials.
Purpose of the Study:
- To develop and test gauge-including projected augmented pseudopotentials for 21 d-elements.
- To calculate chemical shift and quadrupolar coupling constants for inorganic compounds.
- To advance ab initio prediction of nuclear magnetic resonance (NMR) parameters for materials science.
Main Methods:
- Development of gauge-including projected augmented pseudopotentials for 21 d-elements.
- Application of these pseudopotentials to calculate NMR parameters (chemical shift, quadrupolar coupling constant).
- Testing on oxides and nitrides, including semiconductors.
Main Results:
- Successful calculation of chemical shift and quadrupolar coupling constants for d-elements in oxides and nitrides.
- Demonstration of the potential for ab initio prediction of NMR parameters in inorganic materials.
- Establishment of a foundation for using inorganic compounds as NMR chemical shift standards.
Conclusions:
- The developed pseudopotentials represent a significant step towards improved ab initio NMR parameter prediction.
- This work opens avenues for inorganic compounds as alternative NMR chemical shift standards.
- The findings facilitate the use of first-principles calculations to train machine learning models for structural analysis using NMR spectra.
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