Equivariant GNNs

GNN: Graph Neural Network

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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FlashCart: Fast Cartesian Tensor Products for Equivariant Interatomic Potentials
Oct 5, 2026Viktor Zaverkin, Payman Goodarzi, Sergey V. Sukhomlinov +4Machine Learning Interatomic Potentials GPU Kernel Optimization
INM – Leibniz Institute for New Materials · Saarland University · German Research Center for Artificial Intelligence (DFKI) +2
CrossGMN: Graph Metanetworks for Cross-Architecture Weight-Space Transformations
Oct 1, 2026Adir Dayan, Yam Eitan, Haggai MaronModel Compression Hypernetworks
Technion – Israel Institute of Technology · NVIDIA Research
Clifford Sheaf Neural Networks
Oct 1, 2026Kotaro Kamiya, Joel NichollsSheaf Neural Networks Equivariant GNNs
SyntheticGestalt KK
Multi-Attractor GNNs: Set-Valued Expressivity Beyond Unique Equilibria
Sep 28, 2026Jialin LiuGNN Expressivity Combinatorial Optimization
Edge-Level Automorphism in GNNs: A Quantitative Framework and Effective Designs For Link Prediction
Sep 28, 2026Chen Shao, Donald Loveland, Tobias Käfer +1Link Prediction GNN Expressivity
Karlsruhe Institute of Technology Karlsruhe, Germany · Department of Computer Science University of Michigan Ann Arbor, MI, USA · Computer Science & Engineering University of Michigan Ann Arbor, MI, USA
Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs
Aug 28, 2026Alessio Borgi, Mario Severino, Fabrizio Silvestri +1Equivariant Neural Networks Sheaf Neural Networks
Department of Computer Science and Technology, University of Cambridge · Department of Computer, Control and Management Engineering, Sapienza University of Rome · Department of Information Engineering, University of Padua
EnsembleEGNN: Set-Based Graph Learning for Thermodynamic Ensembles of Cyclic Peptides
Jul 23, 2026Aaron L. Feller, Kris Deibler, Maxim SecorEquivariant GNNs Molecular Property Prediction
Interdisciplinary Life Sciences, University of Texas at Austin, Austin, TX. · Molecular AI, Novo Nordisk, Lexington, MA.
GEqTrain: A Configuration-Driven Framework for Retargeting Equivariant Graph Neural Networks Across 3D Scientific Tasks
Jul 21, 2026Daniele Angioletti, Marco Nobile, Vittorio LimongelliFlow Matching Generative Modeling
Faculty of Biomedical Sciences, Euler Institute, Universit´a della Svizzera italiana, 6900 Lugano, Switzerland
Sobek: Streaming Equivariant Tensor Product Convolutions
Jul 20, 2026Vladimir Chorošajev, Cédric BényGPU Kernel Optimization GPU Acceleration
Cortex Discovery
Stability of Flow Models for Graph Signals
Jul 8, 2026Martin Schmidt, Gonzalo MateosGraph Signal Processing Flow Matching
Dept. of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA
Spin-Weighted Spherical Harmonics Enable Complete and Scalable $\mathrm{E}(3)$ -Equivariant Networks
Jul 1, 2026Chenxing Liang, Yuchao Lin, Andrii Kryvenko +5Machine Learning Interatomic Potentials Equivariant Neural Networks
Department of Computer Science and Engineering, Texas A&M University · Lambda, Inc. · Department of Materials Science and Engineering, Texas A&M University +3
Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials
Jun 28, 2026Can Polat, Erchin Serpedin, Mustafa Kurban +1Machine Learning Interatomic Potentials Equivariant GNNs
Dept. of Electrical & Computer Eng. Texas A&M Univ. College Station, TX, USA · Dept. of Prosthetics & Orthotics College of Science & Eng. Ankara Univ., Ankara, Turkey · Hamad Bin Khalifa Univ. and Texas A&M Univ. at Qatar Doha, Qatar
Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening
Jun 17, 2026Kasper Helverskov Petersen, François R J Cornet, Martin Ovesen +3Materials Property Prediction Equivariant GNNs
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark · Department of Physics, Technical University of Denmark, Kongens Lyngby, Denmark
Scalar-pathway fidelity improves physical accuracy in short-range equivariant interatomic potentials
Jun 14, 2026Jia Bi, Alin Marin Elena, Samuel PinillaMachine Learning Interatomic Potentials Equivariant GNNs
Science and Technology Facilities Council, Harwell Campus, Didcot, OX11 0QX, Oxfordshire, United Kingdom. · Science and Technology Facilities Council, Keckwick Lane, Daresbury, WA4 4AD, Cheshire, United Kingdom. · Diamond Light Source, Harwell Science and Innovation Campus,2026 Didcot, OX11 0QX, Oxfordshire, United Kingdom.
EvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation
May 20, 2026Mansoor Ahmed, Sujin Lee, Umar Khayaz +1Protein Language Models Equivariant GNNs
Georgia State University, Atlanta, USA · Georgia Institute of Technology, Atlanta, USA
Velocityformer: Broken-Symmetry-Matched Equivariant Graph Transformers for Cosmological Velocity Reconstruction
May 20, 2026Tilman Tröster, David Mirkovic, Veronika Oehl +1Equivariant Neural Networks Equivariant GNNs
Department of Physics ETH Zürich
Tensor Channel Equivariant Graph Neural Networks for Molecular Polarizability Prediction
May 16, 2026Jean Philip Filling, Daniel Franzen, Michael WandEquivariant GNNs Molecular Property Prediction
Institute for Computer Science, Johannes Gutenberg University Mainz, Germany
QT-Net: Rethinking Evaluation of AI Models in Atomic Chemical Space
May 11, 2026Pablo Martínez Crespo, Stefano Ribes, Martin Rahm +6Graph Neural Networks Quantum Chemistry
Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg · Department of Chemistry and Chemical Engineering Chalmers University of Technology · Technology Research Intel Corporation +3
Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning
May 9, 2026Chen Wang, Siyu Hu, Guangming Tan +1Equivariant GNNs Structured Pruning
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences
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.
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

Oct 5, 2026, cs.LGJ/K move · Enter open · S save

FlashCart: Fast Cartesian Tensor Products for Equivariant Interatomic Potentials

Viktor Zaverkin, Payman Goodarzi, Sergey V. Sukhomlinov, Davit Hovhannisyan+3

INM – Leibniz Institute for New Materials · Saarland University · German Research Center for Artificial Intelligence (DFKI)+2

Machine Learning Interatomic Potentials GPU Kernel Optimization Equivariant GNNs

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Machine-learned interatomic potentials extend atomistic simulations beyond the length- and timescales accessible to electronic-structure methods. However, the computational cost of equivariant architectures limits the local correlations they can represent in practice and therefore their achievable accuracy. Here we introduce FlashCart, which makes higher-order correlations affordable by combining generated GPU kernels with an architecture that recursively builds equivariant features and compresses them to a fixed width at each step. We express tensor products in independent Cartesian components and symbolically simplify them and their derivatives, producing fused kernels that often outperform optimized spherical counterparts. We then show that increasing correlation order improves accuracy more efficiently than increasing width, depth, or tensor rank. On SPICE-MACE-OFF, FlashCart models advance the measured accuracy-efficiency frontier: a model with

5.6

million parameters achieves lower energy and force errors and

10\times

faster inference than a transformer with

189

million parameters.

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