Machine learning interatomic potential study of grain boundaries thermal conductance and tensile strength in hexagonal boron nitride

Chemical vapor deposition enables the scalable synthesis of large-area two-dimensional (2D) materials, but the resulting grain boundaries (GBs) can strongly influence their thermal and mechanical performance. In this work, we employ machine learning interatomic potentials (MLIPs) to systematically i