% Key papers for Equivariant networks for molecules (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2017_neural_36305,
  title = {{Neural Message Passing for Quantum Chemistry}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/529},
}

@inproceedings{icml2020_learning_38411,
  title = {{Learning to Simulate Complex Physics with Graph Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6849},
}

@inproceedings{iclr2020_directional_29238,
  title = {{Directional Message Passing for Molecular Graphs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=B1eWbxStPH},
}

@inproceedings{icml2021_equivariant_39303,
  title = {{Equivariant message passing for the prediction of tensorial properties and molecular spectra}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8499},
}

@inproceedings{neurips2021_gemnet_61816,
  title = {{GemNet: Universal Directional Graph Neural Networks for Molecules}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28288},
}

@inproceedings{iclr2023_equiformer_32421,
  title = {{Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/12104},
}

@inproceedings{neurips2022_comenet_65887,
  title = {{ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52940},
}

@inproceedings{icml2022_se_41130,
  title = {{SE(3) Equivariant Graph Neural Networks with Complete Local Frames}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/18345},
}

@inproceedings{iclr2022_geometric_31435,
  title = {{Geometric and Physical Quantities improve E(3) Equivariant Message Passing}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6225},
}

@inproceedings{neurips2021_directional_62536,
  title = {{Directional Message Passing on Molecular Graphs via Synthetic Coordinates}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27185},
}

@inproceedings{icml2023_efficient_42807,
  title = {{Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24939},
}

@inproceedings{aaai2024_hago_6173,
  title = {{HAGO-Net: Hierarchical Geometric Message Passing for Molecular Representation Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29373},
}

@inproceedings{neurips2022_equivariant_63688,
  title = {{Equivariant Networks for Crystal Structures}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52781},
}

@inproceedings{aaai2025_aerogto_8065,
  title = {{AeroGTO: An Efficient Graph-Transformer Operator for Learning Large-Scale Aerodynamics of 3D Vehicle Geometries}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34083},
}

@inproceedings{iclr2024_long_35400,
  title = {{Long-Short-Range Message-Passing: A Physics-Informed Framework to Capture Non-Local Interaction for Scalable Molecular Dynamics Simulation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17673},
}

@inproceedings{neurips2022_equivariant_66483,
  title = {{Equivariant Graph Hierarchy-Based Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54332},
}

@inproceedings{aaai2023_equivariant_3586,
  title = {{Equivariant Message Passing Neural Network for Crystal Material Discovery}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26673},
}

@inproceedings{aaai2024_a_5053,
  title = {{A Plug-and-Play Quaternion Message-Passing Module for Molecular Conformation Representation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29602},
}

@inproceedings{icml2023_efficient_42945,
  title = {{Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/23719},
}

@inproceedings{neurips2026_equivariant_93582,
  title = {{Equivariant Spherical Transformer for Efficient Molecular Modeling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155430},
}

@inproceedings{iclr2024_clifford_34693,
  title = {{Clifford Group Equivariant Simplicial Message Passing Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18381},
}

@inproceedings{icml2025_evomesh_47754,
  title = {{EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44846},
}

@inproceedings{icml2026_gffmerge_49640,
  title = {{GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=4L8bHiw2Jm},
}

@inproceedings{icml2026_efficient_53397,
  title = {{Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=f9i6VLHT5v},
}

@inproceedings{neurips2025_dualequi_78975,
  title = {{DualEqui: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=kND7h1kD53},
}

@inproceedings{neurips2025_geometric_79801,
  title = {{Geometric Mixture Models for Electrolyte Conductivity Prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=t6tObHgZSt},
}

@inproceedings{iclr2025_improving_82567,
  title = {{Improving Equivariant Networks with Probabilistic Symmetry Breaking}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29230},
}

@inproceedings{iclr2026_ads_85428,
  title = {{AdS-GNN - a Conformally Equivariant Graph Neural Network}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=EIyvsL5Cue},
}

@inproceedings{iclr2026_improving_89279,
  title = {{Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=x66u6TEDUw},
}

@inproceedings{iclr2024_hybrid_33847,
  title = {{Hybrid Directional Graph Neural Network for Molecules}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19228},
}

@inproceedings{icml2026_physics_49867,
  title = {{Physics-informed coarsening for multigrid graph neural networks surrogates}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=6aFl5SegYt},
}

@inproceedings{iclr2025_gotennet_80842,
  title = {{GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30925},
}

@inproceedings{iclr2025_a_81953,
  title = {{A Theoretically-Principled Sparse, Connected, and Rigid Graph Representation of Molecules}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29832},
}

@inproceedings{iclr2025_beyond_82503,
  title = {{Beyond Circuit Connections: A Non-Message Passing Graph Transformer Approach for Quantum Error Mitigation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29291},
}

@inproceedings{iclr2026_a_85696,
  title = {{A Function-Centric Graph Neural Network Approach for Predicting Electron Densities}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=HDdkFjFEZd},
}