Predicting Synthesizable Functional Edge Reconstructions in 2D Monolayers

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) have attracted tremendous interest due to their exceptional electronic and optical properties. More interestingly, it has been found that edges of 2D TMDCs are responsible for their promising catalytic activity, while the basal plane is chemically inert. In addition to the conventional armchair and zigzag edges, more complex edge reconstructions have recently been realized by various experiments. Therefore, it is highly desirable to computationally predict the family of stable edges so as to screen their functional properties. Here we report development of such a computational approach and demonstrate it on the 2D TMDC family of systems such as MoS2 & MoSe2. Starting from configuration ensemble generations, we screen for stable edges using cheaper force-fields or surrogate (Neural-Network-based) models, which are then further refined using DFT-level of theory. We predict many stable edges that are superior for hydrogen evolution reaction (HER). Our study thus provides a comprehensive yet tractable computational approach for predicting synthesizable functional edges in 2D monolayers.