3 papers
physics.comp-ph2026
Unlocking Multi-Component Bulk-Materials Molecular Dynamics with a Small-Footprint Machine Learning Interatomic Potential
Yucheng Ouyang, Xin Chen, Ying Liu +8
Bulk materials, as opposed to nanomaterials, require molecular dynamics (MD) simulations on a large spatial scale (~10^9 atoms or more) to adequately capture their atomic-scale phy…
cond-mat.mtrl-sci2025
Machine learning-driven elasticity prediction in advanced inorganic materials via convolutional neural networks
Yujie Liu, Zhenyu Wang, Hang Lei +6
Inorganic crystal materials have broad application potential due to excellent physical and chemical properties, with elastic properties (shear modulus, bulk modulus) crucial for pr…
physics.comp-ph2025
A Multi-Level Monte Carlo Tree Search Method for Configuration Generation in Crystalline Systems
Xiaoxu Li, Ge Xu, Huajie Chen +2
In this paper, we study the construction of structural models for the description of substitutional defects in crystalline materials. Predicting and designing the atomic structures…