5 papers
Meta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learning
Yifan Li, Arravind Subramanian, Xiaoqing Liu +3
A central challenge in computational catalysis is the identification of low-energy and chemically plausible adsorption configurations, as these directly affect adsorption energies,…
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…
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…
Scalable Crystal Structure Relaxation Using an Iteration-Free Deep Generative Model with Uncertainty Quantification
Ziduo Yang, Yi-Ming Zhao, Xian Wang +6
In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent di…