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Computational prediction of new auxetic materials
Auxetics comprise a rare family of materials that manifest negative Poisson’s ratio, which causes an expansion instead of contraction under tension. Most known homogeneously auxetic materials are porous foams or artificial macrostructures and there are few examples of inorganic materials that exhibi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5567361/ https://www.ncbi.nlm.nih.gov/pubmed/28831161 http://dx.doi.org/10.1038/s41467-017-00399-6 |
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author | Dagdelen, John Montoya, Joseph de Jong, Maarten Persson, Kristin |
author_facet | Dagdelen, John Montoya, Joseph de Jong, Maarten Persson, Kristin |
author_sort | Dagdelen, John |
collection | PubMed |
description | Auxetics comprise a rare family of materials that manifest negative Poisson’s ratio, which causes an expansion instead of contraction under tension. Most known homogeneously auxetic materials are porous foams or artificial macrostructures and there are few examples of inorganic materials that exhibit this behavior as polycrystalline solids. It is now possible to accelerate the discovery of materials with target properties, such as auxetics, using high-throughput computations, open databases, and efficient search algorithms. Candidates exhibiting features correlating with auxetic behavior were chosen from the set of more than 67 000 materials in the Materials Project database. Poisson’s ratios were derived from the calculated elastic tensor of each material in this reduced set of compounds. We report that this strategy results in the prediction of three previously unidentified homogeneously auxetic materials as well as a number of compounds with a near-zero homogeneous Poisson’s ratio, which are here denoted “anepirretic materials”. |
format | Online Article Text |
id | pubmed-5567361 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-55673612017-08-30 Computational prediction of new auxetic materials Dagdelen, John Montoya, Joseph de Jong, Maarten Persson, Kristin Nat Commun Article Auxetics comprise a rare family of materials that manifest negative Poisson’s ratio, which causes an expansion instead of contraction under tension. Most known homogeneously auxetic materials are porous foams or artificial macrostructures and there are few examples of inorganic materials that exhibit this behavior as polycrystalline solids. It is now possible to accelerate the discovery of materials with target properties, such as auxetics, using high-throughput computations, open databases, and efficient search algorithms. Candidates exhibiting features correlating with auxetic behavior were chosen from the set of more than 67 000 materials in the Materials Project database. Poisson’s ratios were derived from the calculated elastic tensor of each material in this reduced set of compounds. We report that this strategy results in the prediction of three previously unidentified homogeneously auxetic materials as well as a number of compounds with a near-zero homogeneous Poisson’s ratio, which are here denoted “anepirretic materials”. Nature Publishing Group UK 2017-08-22 /pmc/articles/PMC5567361/ /pubmed/28831161 http://dx.doi.org/10.1038/s41467-017-00399-6 Text en © The Author(s) 2017 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/. |
spellingShingle | Article Dagdelen, John Montoya, Joseph de Jong, Maarten Persson, Kristin Computational prediction of new auxetic materials |
title | Computational prediction of new auxetic materials |
title_full | Computational prediction of new auxetic materials |
title_fullStr | Computational prediction of new auxetic materials |
title_full_unstemmed | Computational prediction of new auxetic materials |
title_short | Computational prediction of new auxetic materials |
title_sort | computational prediction of new auxetic materials |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5567361/ https://www.ncbi.nlm.nih.gov/pubmed/28831161 http://dx.doi.org/10.1038/s41467-017-00399-6 |
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