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2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches
Two-dimensional (2D) materials have been a hot research topic in the last decade, due to novel fundamental physics in the reduced dimension and appealing applications. Systematic discovery of functional 2D materials has been the focus of many studies. Here, we present a large dataset of 2D materials...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6561947/ https://www.ncbi.nlm.nih.gov/pubmed/31189922 http://dx.doi.org/10.1038/s41597-019-0097-3 |
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author | Zhou, Jun Shen, Lei Costa, Miguel Dias Persson, Kristin A. Ong, Shyue Ping Huck, Patrick Lu, Yunhao Ma, Xiaoyang Chen, Yiming Tang, Hanmei Feng, Yuan Ping |
author_facet | Zhou, Jun Shen, Lei Costa, Miguel Dias Persson, Kristin A. Ong, Shyue Ping Huck, Patrick Lu, Yunhao Ma, Xiaoyang Chen, Yiming Tang, Hanmei Feng, Yuan Ping |
author_sort | Zhou, Jun |
collection | PubMed |
description | Two-dimensional (2D) materials have been a hot research topic in the last decade, due to novel fundamental physics in the reduced dimension and appealing applications. Systematic discovery of functional 2D materials has been the focus of many studies. Here, we present a large dataset of 2D materials, with more than 6,000 monolayer structures, obtained from both top-down and bottom-up discovery procedures. First, we screened all bulk materials in the database of Materials Project for layered structures by a topology-based algorithm and theoretically exfoliated them into monolayers. Then, we generated new 2D materials by chemical substitution of elements in known 2D materials by others from the same group in the periodic table. The structural, electronic and energetic properties of these 2D materials are consistently calculated, to provide a starting point for further material screening, data mining, data analysis and artificial intelligence applications. We present the details of computational methodology, data record and technical validation of our publicly available data (http://www.2dmatpedia.org/). |
format | Online Article Text |
id | pubmed-6561947 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65619472019-06-21 2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches Zhou, Jun Shen, Lei Costa, Miguel Dias Persson, Kristin A. Ong, Shyue Ping Huck, Patrick Lu, Yunhao Ma, Xiaoyang Chen, Yiming Tang, Hanmei Feng, Yuan Ping Sci Data Data Descriptor Two-dimensional (2D) materials have been a hot research topic in the last decade, due to novel fundamental physics in the reduced dimension and appealing applications. Systematic discovery of functional 2D materials has been the focus of many studies. Here, we present a large dataset of 2D materials, with more than 6,000 monolayer structures, obtained from both top-down and bottom-up discovery procedures. First, we screened all bulk materials in the database of Materials Project for layered structures by a topology-based algorithm and theoretically exfoliated them into monolayers. Then, we generated new 2D materials by chemical substitution of elements in known 2D materials by others from the same group in the periodic table. The structural, electronic and energetic properties of these 2D materials are consistently calculated, to provide a starting point for further material screening, data mining, data analysis and artificial intelligence applications. We present the details of computational methodology, data record and technical validation of our publicly available data (http://www.2dmatpedia.org/). Nature Publishing Group UK 2019-06-12 /pmc/articles/PMC6561947/ /pubmed/31189922 http://dx.doi.org/10.1038/s41597-019-0097-3 Text en © The Author(s) 2019 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/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Zhou, Jun Shen, Lei Costa, Miguel Dias Persson, Kristin A. Ong, Shyue Ping Huck, Patrick Lu, Yunhao Ma, Xiaoyang Chen, Yiming Tang, Hanmei Feng, Yuan Ping 2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches |
title | 2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches |
title_full | 2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches |
title_fullStr | 2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches |
title_full_unstemmed | 2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches |
title_short | 2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches |
title_sort | 2dmatpedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6561947/ https://www.ncbi.nlm.nih.gov/pubmed/31189922 http://dx.doi.org/10.1038/s41597-019-0097-3 |
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