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Found 6 papers, 2 papers with code
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
Geometric graph is a special kind of graph with geometric features, which is vital to model many scientific problems.
Equivariant Pretrained Transformer for Unified Geometric Learning on Multi-Domain 3D Molecules
Pretraining on a large number of unlabeled 3D molecules has showcased superiority in various scientific applications.
Space Group Constrained Crystal Generation
Crystals are the foundation of numerous scientific and industrial applications.
Crystal Structure Prediction by Joint Equivariant Diffusion
To be specific, DiffCSP jointly generates the lattice and atom coordinates for each crystal by employing a periodic-E(3)-equivariant denoising model, to better model the crystal geometry.
Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs
Pretraining molecular representation models without labels is fundamental to various applications.
Alternated Training with Synthetic and Authentic Data for Neural Machine Translation
In this work, we propose alternated training with synthetic and authentic data for NMT.
