equivariant-graph-neural-network

[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

MatTen: Equivariant Graph Neural Nets for Tensorial Properties of Materials

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

Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures

Implementation of the Confidence Bootstrapping procedure for protein-ligand docking.

STRUCTURES25 - Machine-learning OF-DFT implementation using equivariant graph neural networks (JACS 2025).

Rotationally Equivariant Hypergraph Neural Networks (EquiHGNN)

TESGNN: 3D Temporal Equivariant Scene Graph Neural Networks (published at TMLR)

E(3)-Equivariant Mesh Neural Networks (AISTATS 2024)

This is repository for "Automated transition state generation for mechanistic exploration in organic synthesis"

Mandala is an E(3)-Equivariant Graph Neural Network Framework for Learning Electronic-Structure Operators with Observable Guidance based on e3nn.

OM-Diff: Inverse-design of organometallic catalysts with guided equivariant denoising diffusion

Employing Molecular Conformations for Ligand-based Virtual Screening with Equivariant Graph Neural Network and Deep Multiple Instance Learning

This repository provides implementations for sparse graph separation, power graph experiments, and chemical property prediction.

Implementation of Geometrically Equivariant Graph Neural Networks (GNNs) in PyTorch.

Using high-throughput DFT, Wannier90, and TB2J, this project calculates the magnetic exchange interactions of 2D CrI3 under mechanical strain to reveal how structural distortions modulate its Heisenberg parameters.

A mathematical/computational framework that upgrades the complex numbers for working with unified 2D coordinates, inversive geometry, Eisenstein integers, radial dual triangular lattice graph (RDTLG), hexagonal symmetries, signal processing, equivariant encodings, and geometric deep learning.

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