Equivariant GNNs

GNN: Graph Neural Network

Equivariant graph neural networks study how to make predictions that transform consistently when inputs are rotated, translated, permuted, or changed by other symmetries. Common approaches use scalar, vector, and higher-order tensor features with message passing, spherical harmonics and tensor products, or learned geometric transport between nodes. These methods are used for molecular and materials properties, physical dynamics, protein structures, and graph tasks where respecting geometry can improve data efficiency and generalization. A central challenge is balancing expressive symmetry-aware models with their computational cost, while matching the encoded symmetries to real settings where symmetry may be broken.

Latest papers

43 papers11 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions
May 4, 2026Sergio Orozco, Tushar Kusnur, Brandon May +2Deformable Object Manipulation Robotic Manipulation
Brown University, Providence, RI · Robotics and AI (RAI) Institute, Cambridge, MA · General Motors
PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies
May 2, 2026Viet Thanh Duy Nguyen, John K. Johnstone, Truong-Son HyGraph Representation Learning Hierarchical Representation Learning
University of Alabama at Birmingham Birmingham, Alabama, USA
Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
Apr 22, 2026Ali Rayat, Yaohang Li, Gia-Wei ChernMessage Passing Neural Networks High-Energy Physics
Department of Physics, University of Virginia, Charlottesville, VA 22904, USA · Department of Computer Science, Old Dominion University, Norfolk, VA 22904, USA.
XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures
Apr 13, 2026Vitor F. Grizzi, Luke N. Pretzie, Jiayi Xu +1Materials Property Prediction Equivariant GNNs
Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USA
EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers
Apr 10, 2026Yi-Lun Liao, Alexander J. Hoffman, Sabrina C. Shen +3Equivariant GNNs Graph Transformer
Massachusetts Institute of Technology (MIT) · Mirror Physics · Entalpic
GDEGAN: Gaussian Dynamic Equivariant Graph Attention Network for Ligand Binding Site Prediction
Mar 20, 2026Animesh, Plaban Kumar Bhowmick, Pralay MitraGraph Attention Networks Equivariant GNNs
Department of Artificial Intelligence, Indian Institute of Technology, Kharagpur, India · Department of Computer Science, Indian Institute of Technology, Kharagpur, India
Any-Subgroup Equivariant Networks via Symmetry Breaking
Mar 19, 2026Abhinav Goel, Derek Lim, Hannah Lawrence +2Equivariant Neural Networks Equivariant GNNs
MIT · TUM · Flatiron Institute
Preserving Continuous Symmetry in Discrete Spaces: Geometric-Aware Quantization for SO(3)-Equivariant GNNs
Mar 5, 2026Haoyu Zhou, Ping Xue, Hao Zhang +1Equivariant GNNs Neural Network Quantization
Nanjing University, Nanjing, China. · Gusu Lab, Suzhou, China.
Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory
Mar 3, 2026Xuan Zhang, Haiyang Yu, Chengdong Wang +3Quantum Chemistry Density Functional Theory
Department of Computer Science and Engineering, Texas A&M University · Department of Materials Science and Engineering, Texas A&M University · J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University +2
Graph neural network force fields for adiabatic dynamics of lattice Hamiltonians
Mar 2, 2026Yunhao Fan, Gia-Wei ChernGraph Neural Networks Hamiltonian Dynamics
Department of Physics, University of Virginia, Charlottesville, VA 22904, USA
Balancing Symmetry and Efficiency in Graph Flow Matching
Feb 20, 2026Benjamin Honoré, Alba Carballo-Castro, Yiming Qin +1Generative Modeling Equivariant GNNs
LTS4, EPFL, Lausanne, Switzerland
Beyond Topology: A Morphological Symmetry Graph Representation for Locomotion Policy Learning
Nov 30, 2025Sizhe Wei, Xulin Chen, Fengze Xie +3RL for Legged Locomotion Equivariant GNNs
Georgia Institute of Technology · Syracuse University · California Institute of Technology
Roto-translated Local Coordinate Frames For Interacting Dynamical Systems
Oct 28, 2021Miltiadis Kofinas, Naveen Shankar Nagaraja, Efstratios GavvesDynamical Systems Equivariant GNNs
University of Amsterdam · BMW Group

May 4, 2026J/K move · Enter open · S save

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions

Sergio Orozco, Tushar Kusnur, Brandon May, George Konidaris+1

Brown University, Providence, RI · Robotics and AI (RAI) Institute, Cambridge, MA · General Motors

Deformable Object Manipulation Robotic Manipulation Equivariant GNNs Physics-Informed ML Physics-Informed GNNs Deformable Object Simulation

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Learning data-efficient object dynamics models for robotic manipulation remains challenging, especially for deformable objects. A popular approach is to model objects as sets of 3D particles and learn their motion using graph neural networks. In practice, this is not enough to maintain physical feasibility over long horizons and may require large amounts of interaction data to learn. We introduce PIEGraph, a novel approach to combining analytical physics and data-driven models to capture object dynamics for both rigid and deformable bodies using limited real-world interaction data. PIEGraph consists of two components: (1) a Physically Informed particle-based analytical model (implemented as a spring--mass system) to enforce physically feasible motion, and (2) an Equivariant Graph Neural Network with a novel action representation that exploits symmetries in particle interactions to guide the analytical model. We evaluate PIEGraph in simulation and on robot hardware for reorientation and repositioning tasks with ropes, cloth, stuffed animals and rigid objects. We show that our method enables accurate dynamics prediction and reliable downstream robotic manipulation planning, which outperforms state of the art baselines.