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GIPAW Pseudopotentials of d Elements for Solid-State NMR
Computational methods are increasingly used to support interpreting, assigning and predicting the solid-state nuclear resonance magnetic spectra of materials. Currently, density functional theory is seen to achieve a good balance between efficiency and accuracy in solid-state chemistry. To be specif...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101793/ https://www.ncbi.nlm.nih.gov/pubmed/35591680 http://dx.doi.org/10.3390/ma15093347 |
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author | Tantardini, Christian Kvashnin, Alexander G. Ceresoli, Davide |
author_facet | Tantardini, Christian Kvashnin, Alexander G. Ceresoli, Davide |
author_sort | Tantardini, Christian |
collection | PubMed |
description | Computational methods are increasingly used to support interpreting, assigning and predicting the solid-state nuclear resonance magnetic spectra of materials. Currently, density functional theory is seen to achieve a good balance between efficiency and accuracy in solid-state chemistry. To be specific, density functional theory allows the assignment of signals in nuclear resonance magnetic spectra to specific sites and can help identify overlapped or missing signals from experimental nuclear resonance magnetic spectra. To avoid the difficulties correlated to all-electron calculations, a gauge including the projected augmented wave method was introduced to calculate nuclear resonance magnetic parameters with great success in organic crystals in the last decades. Thus, we developed a gauge including projected augmented pseudopotentials of 21 d elements and tested them on, respectively, oxides or nitrides (semiconductors), calculating chemical shift and quadrupolar coupling constant. This work can be considered the first step to improving the ab initio prediction of nuclear magnetic resonance parameters, and leaves open the possibility for inorganic compounds to constitute an alternative standard compound, with respect to tetramethylsilane, to calculate the chemical shift. Furthermore, this work represents the possibility to obtain results from first-principles calculations, to train a machine-learning model to solve or refine structures using predicted nuclear magnetic resonance spectra. |
format | Online Article Text |
id | pubmed-9101793 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91017932022-05-14 GIPAW Pseudopotentials of d Elements for Solid-State NMR Tantardini, Christian Kvashnin, Alexander G. Ceresoli, Davide Materials (Basel) Article Computational methods are increasingly used to support interpreting, assigning and predicting the solid-state nuclear resonance magnetic spectra of materials. Currently, density functional theory is seen to achieve a good balance between efficiency and accuracy in solid-state chemistry. To be specific, density functional theory allows the assignment of signals in nuclear resonance magnetic spectra to specific sites and can help identify overlapped or missing signals from experimental nuclear resonance magnetic spectra. To avoid the difficulties correlated to all-electron calculations, a gauge including the projected augmented wave method was introduced to calculate nuclear resonance magnetic parameters with great success in organic crystals in the last decades. Thus, we developed a gauge including projected augmented pseudopotentials of 21 d elements and tested them on, respectively, oxides or nitrides (semiconductors), calculating chemical shift and quadrupolar coupling constant. This work can be considered the first step to improving the ab initio prediction of nuclear magnetic resonance parameters, and leaves open the possibility for inorganic compounds to constitute an alternative standard compound, with respect to tetramethylsilane, to calculate the chemical shift. Furthermore, this work represents the possibility to obtain results from first-principles calculations, to train a machine-learning model to solve or refine structures using predicted nuclear magnetic resonance spectra. MDPI 2022-05-06 /pmc/articles/PMC9101793/ /pubmed/35591680 http://dx.doi.org/10.3390/ma15093347 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tantardini, Christian Kvashnin, Alexander G. Ceresoli, Davide GIPAW Pseudopotentials of d Elements for Solid-State NMR |
title | GIPAW Pseudopotentials of d Elements for Solid-State NMR |
title_full | GIPAW Pseudopotentials of d Elements for Solid-State NMR |
title_fullStr | GIPAW Pseudopotentials of d Elements for Solid-State NMR |
title_full_unstemmed | GIPAW Pseudopotentials of d Elements for Solid-State NMR |
title_short | GIPAW Pseudopotentials of d Elements for Solid-State NMR |
title_sort | gipaw pseudopotentials of d elements for solid-state nmr |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101793/ https://www.ncbi.nlm.nih.gov/pubmed/35591680 http://dx.doi.org/10.3390/ma15093347 |
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