2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches

By Jun Zhou, Lei Shen, M. D. Costa, Kristin A. Persson, Shyue Ping Ong, P. Huck, Yunhao Lu, Xiaoyang Ma, Yiming Chen, Hanmei Tang and 1 more. Scientific Data, 2019

332 citations recorded by the source corpus.

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/). Design Type(s) modeling and simulation objective • material transformation objective Measurement Type(s) material entity Technology Type(s) digital curation Factor Type(s) Approach • element • material property Sample Characteristic(s) Design Type(s) modeling and simulation objective • material transformation objective Measurement Type(s) material entity Technology Type(s) digital curation Factor Type(s) Approach • element • material property Sample Characteristic(s) Machine-accessible metadata file describing the reported data (ISA-Tab format)

Subfields: Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/a7961e750477cd3829853521bfb2c46a83a79294