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


  • 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.

published proceedings

  • J Phys Chem Lett

altmetric score

  • 1.25

author list (cited authors)

  • Hong, S., Nomura, K., Krishnamoorthy, A., Rajak, P., Sheng, C., Kalia, R. K., Nakano, A., & Vashishta, P.

citation count

  • 20

complete list of authors

  • Hong, Sungwook||Nomura, Ken-Ichi||Krishnamoorthy, Aravind||Rajak, Pankaj||Sheng, Chunyang||Kalia, Rajiv K||Nakano, Aiichiro||Vashishta, Priya

publication date

  • January 2019