4 papers
An Efficient High-Degree, High-Order Equivariant Graph Neural Network for Direct Crystal Structure Optimization
Ziduo Yang, Wei Zhuo, Huiqiang Xie +2
Crystal structure optimization is fundamental to materials modeling but remains computationally expensive when performed with density-functional theory (DFT). Machine-learning (ML)…
Equivariant Atomic and Lattice Modeling Using Geometric Deep Learning for Crystal Structure Optimization
Ziduo Yang, Yi-Ming Zhao, Xian Wang +3
Structure optimization, which yields the relaxed structure (minimum-energy state), is essential for reliable materials property calculations, yet traditional ab initio approaches s…
Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections
Wei Zhuo, Zhaohuan Zhan, Han Yu
Federated Learning (FL) on graph-structured data typically faces non-IID challenges, particularly in scenarios where each client holds a distinct subgraph sampled from a global gra…
Modeling crystal defects using defect-informed neural networks
Ziduo Yang, Xiaoqing Liu, Xiuying Zhang +3
Most AI-for-Materials research to date has focused on ideal crystals, whereas real-world materials inevitably contain defects that play a critical role in modern functional technol…