Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks (ICML 2023)

[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs

EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers

High-performance CUDA kernels for equivariant graph neural networks (MACE, NequIP, Allegro). 10-20x faster than e3nn.

[TMLR 2024 J2C Certification] Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields

Fully-Differentiable Tensor Spherical Harmonics in JAX

Interactive exploration of equivariant neural networks on homogeneous spaces, with a focus on the sphere S² as SO(3)/SO(2). From Lecture 8 of the Lie groups course with Quantum Formalism

EOSNet: Graph neural network with Gaussian Overlap Matrix (GOM) fingerprints for predicting material properties. Supports CGCNN and e3nn equivariant backbones, s and s+p orbitals, and differentiable GOM for MLIP energy/force prediction.

Implement SE(3)-equivariant graph attention transformers for efficient and expressive molecular modeling in PyTorch.