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  • 标题:2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches
  • 本地全文:下载
  • 作者:Jun Zhou ; Lei Shen ; Miguel Dias Costa
  • 期刊名称:Scientific Data
  • 电子版ISSN:2052-4463
  • 出版年度:2019
  • 卷号:6
  • DOI:10.1038/s41597-019-0097-3
  • 语种:English
  • 出版社:Nature Publishing Group
  • 摘要: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/ ).
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