An efficient algorithm for novel two-dimensional crystal structure prediction

Two-dimensional (2D) materials are promising for their intriguing standalone properties and a number of combinatorial heterostructures. In this regard, crystal structure prediction (CSP) can enhance both material and property spaces significantly, accelerating innovative materials discovery. However, conventional approaches based on global optimization may be inefficient for 2D CSP due to enormously enhanced search space. Here, we will discuss an efficient algorithm for predicting novel 2D materials based on spatial symmetry of the atomic arrangements. We show the method was used to predict a number of novel 2D silicon crystals [1] and group IV and group VI compounds (namely, TXene) [2]. In addition, we will show that the method is efficient and transferable, and can be further applied to propose novel 2D materials.