3 papers
cs.LG2026
Preserving Continuous Symmetry in Discrete Spaces: Geometric-Aware Quantization for SO(3)-Equivariant GNNs
Haoyu Zhou, Ping Xue, Hao Zhang +1
Equivariant Graph Neural Networks (GNNs) are essential for physically consistent molecular simulations but suffer from high computational costs and memory bottlenecks, especially w…
cond-mat.mtrl-sci2024
VQCrystal: Leveraging Vector Quantization for Discovery of Stable Crystal Structures
ZiJie Qiu, Luozhijie Jin, Zijian Du +5
Discovering functional crystalline materials through computational methods remains a formidable challenge in materials science. Here, we introduce VQCrystal, an innovative deep lea…
cond-mat.mtrl-sci2024
CTGNN: Crystal Transformer Graph Neural Network for Crystal Material Property Prediction
Zijian Du, Luozhijie Jin, Le Shu +4
The combination of deep learning algorithm and materials science has made significant progress in predicting novel materials and understanding various behaviours of materials. Here…