Modulating Roughness and Ripple Dynamics in Hexagonal Boron Nitride via Defect Engineering.

Area of Science:

• Materials Science
• Condensed Matter Physics
• Computational Materials Science

Background:

• Two-dimensional (2D) materials like hexagonal boron nitride (hBN) possess intrinsic roughness and thermal rippling.
• The impact of atomic defects on these dynamics in hBN is not well understood.
• Existing models for hBN have limitations in capturing defect energies and dynamics.

Purpose of the Study:

• To develop an accurate computational model for simulating hBN, including defect energies and dynamics.
• To investigate the influence of various atomic defects on the rippling and corrugation of hBN sheets.
• To compare the defect-induced dynamics in hBN with those in graphene.

Main Methods:

• Development of a machine learning-based Atomic Cluster Expansion (ACE) potential for hBN.
• Atomistic simulations using the developed ACE potential to model defect structures and dynamics.
• Analysis of defect concentration-dependent changes in surface roughness and rippling.

Main Results:

• A new, more stable defect structure in hBN consisting of a triplet of square-octagon (4|8) defects was predicted.
• A crossover in rippling behavior was observed with increasing defect density, transitioning from random rippling to fixed corrugation.
• Stone-Wales (5|7) defects were found to have the most significant impact on hBN rippling, unlike in graphene where divacancies dominate.

Conclusions:

• The study introduces a novel ACE potential for accurate hBN simulations, capturing defect properties.
• Atomic defects significantly influence hBN's surface dynamics, with Stone-Wales defects playing a key role.
• Material-specific defect engineering in hBN can be used to tune its properties for applications in nanoelectronics and membranes.