Xiaoying Zhuang

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

Abstract 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 potential...

On-demand inverse design of metamaterials via denoising diffusion probabilistic models

Abstract The nonlinear and non-unique relationship between unit-cell topology and bandgap properties motivates the development of complementary data-driven approaches for metamaterial inverse design. This work presents a conditional denoising diffusion probabilistic model (DDPM)-...

Towards unified AI-driven fracture mechanics: the extended deep energy method (XDEM)

Abstract Physics-Informed Neural Networks (PINNs) have recently emerged as powerful tools for solving partial differential equations (PDEs), with the Deep Energy Method (DEM) proving especially effective in fracture mechanics due to its energy-based formulation. Despite these adv...