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A database of low-energy atomically precise nanoclusters
The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but the structures of the clusters can be computationally expensive to predict. In this work, we present the largest database of cluster structures and properties determined usin...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199895/ https://www.ncbi.nlm.nih.gov/pubmed/37210383 http://dx.doi.org/10.1038/s41597-023-02200-4 |
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author | Manna, Sukriti Wang, Yunzhe Hernandez, Alberto Lile, Peter Liu, Shanping Mueller, Tim |
author_facet | Manna, Sukriti Wang, Yunzhe Hernandez, Alberto Lile, Peter Liu, Shanping Mueller, Tim |
author_sort | Manna, Sukriti |
collection | PubMed |
description | The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but the structures of the clusters can be computationally expensive to predict. In this work, we present the largest database of cluster structures and properties determined using ab-initio methods to date. We report the methodologies used to discover low-energy clusters as well as the energies, relaxed structures, and physical properties (such as relative stability, HOMO-LUMO gap among others) for 63,015 clusters across 55 elements. We have identified clusters for 593 out of 1595 cluster systems (element-size pairs) explored by literature that have energies lower than those reported in literature by at least 1 meV/atom. We have also identified clusters for 1320 systems for which we were unable to find previous low-energy structures in the literature. Patterns in the data reveal insights into the chemical and structural relationships among the elements at the nanoscale. We describe how the database can be accessed for future studies and the development of nanocluster-based technologies. |
format | Online Article Text |
id | pubmed-10199895 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101998952023-05-22 A database of low-energy atomically precise nanoclusters Manna, Sukriti Wang, Yunzhe Hernandez, Alberto Lile, Peter Liu, Shanping Mueller, Tim Sci Data Data Descriptor The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but the structures of the clusters can be computationally expensive to predict. In this work, we present the largest database of cluster structures and properties determined using ab-initio methods to date. We report the methodologies used to discover low-energy clusters as well as the energies, relaxed structures, and physical properties (such as relative stability, HOMO-LUMO gap among others) for 63,015 clusters across 55 elements. We have identified clusters for 593 out of 1595 cluster systems (element-size pairs) explored by literature that have energies lower than those reported in literature by at least 1 meV/atom. We have also identified clusters for 1320 systems for which we were unable to find previous low-energy structures in the literature. Patterns in the data reveal insights into the chemical and structural relationships among the elements at the nanoscale. We describe how the database can be accessed for future studies and the development of nanocluster-based technologies. Nature Publishing Group UK 2023-05-20 /pmc/articles/PMC10199895/ /pubmed/37210383 http://dx.doi.org/10.1038/s41597-023-02200-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Manna, Sukriti Wang, Yunzhe Hernandez, Alberto Lile, Peter Liu, Shanping Mueller, Tim A database of low-energy atomically precise nanoclusters |
title | A database of low-energy atomically precise nanoclusters |
title_full | A database of low-energy atomically precise nanoclusters |
title_fullStr | A database of low-energy atomically precise nanoclusters |
title_full_unstemmed | A database of low-energy atomically precise nanoclusters |
title_short | A database of low-energy atomically precise nanoclusters |
title_sort | database of low-energy atomically precise nanoclusters |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199895/ https://www.ncbi.nlm.nih.gov/pubmed/37210383 http://dx.doi.org/10.1038/s41597-023-02200-4 |
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