Defect Dynamics in 2-D MoS₂ Probed by Using Machine Learning, Atomistic Simulations, and High-Resolution Microscopy.

Area of Science:

• Materials Science
• Nanotechnology
• Condensed Matter Physics

Background:

• Structural defects critically influence the properties of 2D transition-metal dichalcogenides (TMDs).
• Understanding defect dynamics and distribution is vital for nanotechnology but limited by simulation timescales and experimental resolution.
• Existing methods struggle to capture defect behavior at relevant spatiotemporal scales.

Purpose of the Study:

• To investigate the structure, dynamics, and phase transition induction of point defects in monolayer MoS₂.
• To overcome limitations in simulating defect evolution and experimental observation.
• To elucidate the role of defects in the semiconducting (2H) to metallic (1T) phase transition.

Main Methods:

• Supervised machine learning combined with in situ high-resolution transmission electron microscopy (HRTEM).
• Molecular dynamics (MD) simulations integrated with genetic algorithms (GA) for structural optimization.
• Analysis of sulfur vacancy distribution and evolution under varying densities and conditions.

Main Results:

• Sulfur vacancies in MoS₂ preferentially organize into extended lines, irrespective of defect density.
• HRTEM confirms these findings and shows localized 2H-to-1T phase transformation near defects under electron beam irradiation.
• MD simulations reveal the mechanism of phase transition, indicating retention of the 1T phase with increased defect concentration and temperature.

Conclusions:

• Defect organization into lines is energetically favorable in 2D TMDs.
• Extended defects act as nucleation sites for phase transitions, impacting material properties.
• This research provides crucial insights for engineering nanoscale devices based on 2D TMDs.