Unveiling the unexpected sinking and embedding dynamics of surface supported Mo/S clusters on 2D MoS2 with active machine learning

Abstract

Surface-supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition. They will significantly impact the electronic/magnetic properties. Moreover, surface supported atoms are also widely explored for high active and selecting catalysts. Severe deformation, even dipping into the surface, of these clusters can be expected because of the very active edge of clusters and strong interaction between supported clusters and surfaces. However, most models of these clusters are supposed to simply float on the top of the surface because ab initio simulations cannot afford the complex reconstructions. Here, we develop an accurate graph neural network machine learning potential (MLP) from ab initio data by active learning architecture through fine-tuning pre-trained models, and then employ the MLP into Monte Carlo to explore the structural evolutions of Mo and S clusters (1–8 atoms) on perfect and various defective MoS₂ monolayers. Interestingly, Mo clusters can always sink and embed themselves into MoS₂ layers. In contrast, S clusters float on perfect surfaces. On the defective surface, a few S atoms will fill the vacancy and rest S clusters float on the top. Such significant structural reconstructions should be carefully taken into account.