Defect Healing in Layered Materials: A Machine Learning-Assisted Characterization of MoS2 Crystal Phases.

Abstract

Monolayer MoS2 is an outstanding candidate for a next-generation semiconducting material because of its exceptional physical, chemical, and mechanical properties. To make this promising layered material applicable to nanostructured electronic applications, synthesis of a highly crystalline MoS2 monolayer is vitally important. Among different types of synthesis methods, chemical vapor deposition (CVD) is the most practical way to synthesize few- or mono-layer MoS2 on the target substrate owing to its simplicity and scalability. However, synthesis of a highly crystalline MoS2 layer remains elusive. This is because of the number of grains and defects unavoidably generated during CVD synthesis. Here, we perform multimillion-atom reactive molecular dynamics (RMD) simulations to identify an origin of the grain growth, migration, and defect healing process on a CVD-grown MoS2 monolayer. RMD results reveal that grain boundaries could be successfully repaired by multiple heat treatments. Our work proposes a new way of controlling the grain growth and migration on a CVD-grown MoS2 monolayer.