Automatic structure searches have been very successful in predicting structures of bulk materials. It seems out of question whether the automatic search is advantageous over traditional structure design based on chemical knowledge and intuition. Here we give several examples of computational materials, both related to structure search and design. In the first example, we show the development of an efficient method that can automatically explore the surface structures by virtue of structure swarm intelligence. While applying the method on the "simple" diamond (100) surface, we discovered a hitherto unexpected surface reconstruction featuring self-assembly of carbon nanotubes (CNTs) arrays. In the second example, we propose and demonstrate a large family of two-dimensional semiconductors (2DSC), all adopting the same structure and consisting of only main group elements. We demonstrate the attainability of these materials, and show that they cover a large range of lattice constants, band gaps and band edge states, therefore are good candidate materials for heterojunctions. 2DSCs are currently the focus of many studies. Comparing with the 3D semiconductors, the choice of the 2D materials is very limited. The new 2DSCs may pave a way toward fabrication of 2DSC devices at the same thriving level as 3D semiconductors. Furthermore, I will a set of new Carbon and Boron allotropes that are designed by both the structural and topology analysis and the automatic structure search. These allotropes, such as egg-tray and bubble-wrap graphene and borophene in true honey-comb lattice, show superlative properties that can improve the functionality and performance of graphene and other 2D materials. Our examples show that automatic structure search and chemical knowledge and rules can both be important in 2D materials discovery and can benefit from each other.

Automatic structure search versus chemical rules in 2D materials study

2019 Fall National Meeting

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.