Structure and Dynamics of TMDC Moiré Heterostructures: Quantum Dynamics and Machine Learning Simulations

Machine learning (ML) is revolutionizing scientific and engineering disciplines due to its ability to capture hidden patterns in large amounts of data. The recent success of ML can be attributed to increasing amount of information and data, increasing amount of simulation resources, and improving understanding of statistical inference. In this talk I will discuss two topics:

1. Wrinkles, Ridges, Miura-Ori, and Moiré Patterns in MoSe₂

Effects of lateral compression on out-of-plane deformation of two-dimensional (2D) MoSe₂ layers are investigated. A MoSe₂ monolayer develops periodic wrinkles under uniaxial compression and Miura-Ori patterns under biaxial compression. The energetics, mechanical response, defect structure, and dynamics are analyzed as bilayers undergo wrinkle−ridge transformations under uniaxial compression and moiré transformations under biaxial compression. Our results indicate that in-plane compression can induce self-assembly of out-of-plane metasurfaces with controllable semiconducting and metallic phases and moiré patterns.

1. Formation and Control of Ferroelectric Domains in Moiré MoS₂

Moiré supercells, formed by the stacking of 2D van der Waals materials with small twists, have been shown to exhibit novel electronic and optical properties. These supercells have also been observed to give rise to a superlattice of out-of-plane ferroelectric domains. We examine how the initial twist angle of the stacked 2D materials affects the formation of ferroelectric domains and evolution of these domains with increasing temperature.

This research is carried in collaboration with Anikeya Aditya, Rajiv K. Kalia, Aiichiro Nakano and Ken-ichi Nomura of University of Southern California.