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

@inproceedings{iclr2022_geodiff_31253,
  title = {{GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/7028},
}

@inproceedings{neurips2021_geomol_62630,
  title = {{GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26407},
}

@inproceedings{icml2021_learning_39522,
  title = {{Learning Gradient Fields for Molecular Conformation Generation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8519},
}

@inproceedings{iclr2023_pre_33305,
  title = {{Pre-training via Denoising for Molecular Property Prediction}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11588},
}

@inproceedings{iclr2021_learning_30636,
  title = {{Learning Neural Generative Dynamics for Molecular Conformation Generation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2628},
}

@inproceedings{neurips2021_flow_61581,
  title = {{Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26729},
}

@inproceedings{aaai2023_diffmd_3460,
  title = {{DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25663},
}

@inproceedings{icml2022_3dlinker_40137,
  title = {{3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/18143},
}

@inproceedings{icml2022_generative_40624,
  title = {{Generative Coarse-Graining of Molecular Conformations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16717},
}

@inproceedings{icml2021_an_39047,
  title = {{An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8721},
}

@inproceedings{iclr2023_equivariant_33230,
  title = {{Equivariant Energy-Guided SDE for Inverse Molecular Design}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11589},
}

@inproceedings{neurips2024_transferable_70957,
  title = {{Transferable Boltzmann Generators}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96246},
}

@inproceedings{icml2024_swallowing_44016,
  title = {{Swallowing the Bitter Pill: Simplified Scalable Conformer Generation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34436},
}

@inproceedings{neurips2020_torsionnet_60981,
  title = {{TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18176},
}

@inproceedings{icml2023_a_42871,
  title = {{A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24738},
}

@inproceedings{neurips2023_equivariant_69080,
  title = {{Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70795},
}

@inproceedings{iclr2024_unified_34288,
  title = {{Unified Generative Modeling of 3D Molecules with Bayesian Flow Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18787},
}

@inproceedings{icml2023_fractional_43122,
  title = {{Fractional Denoising for 3D Molecular Pre-training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24062},
}

@inproceedings{neurips2023_stochastic_67800,
  title = {{Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71958},
}

@inproceedings{neurips2024_et_72884,
  title = {{ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94522},
}

@inproceedings{iclr2024_gradual_34007,
  title = {{Gradual Optimization Learning for Conformational Energy Minimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19068},
}

@inproceedings{neurips2024_generative_74562,
  title = {{Generative Modeling of Molecular Dynamics Trajectories}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93022},
}

@inproceedings{iclr2025_stiefel_80981,
  title = {{Stiefel Flow Matching for Moment-Constrained Structure Elucidation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30787},
}

@inproceedings{aaai2025_generalized_9321,
  title = {{Generalized Implicit Neural Representations for Dynamic Molecular Surface Modeling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32072},
}

@inproceedings{icml2026_protein_53541,
  title = {{Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=gRzdGEtn3T},
}

@inproceedings{aaai2025_learning_9729,
  title = {{Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34071},
}

@inproceedings{iclr2024_sliced_35149,
  title = {{Sliced Denoising: A Physics-Informed Molecular Pre-Training Method}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17924},
}

@inproceedings{icml2025_wgformer_45985,
  title = {{WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46561},
}

@inproceedings{icml2026_vecmol_49638,
  title = {{VecMol: Vector-Field Representations for 3D Molecule Generation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=4K87H0eSqu},
}

@inproceedings{icml2026_coarse_50690,
  title = {{Coarse-Grained Boltzmann Generators}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=EsB46shM3U},
}

@inproceedings{icml2026_a_52707,
  title = {{A Unified Density Operator View of Flow Control and Merging}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=YKgp1KiZcB},
}

@inproceedings{icml2026_flexiflow_53750,
  title = {{FlexiFlow: decomposable flow matching for generation of flexible molecular ensemble}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=iL4Uo9HeXc},
}

@inproceedings{neurips2024_neural_71309,
  title = {{Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95938},
}

@inproceedings{neurips2025_jamun_75485,
  title = {{JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensemble Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=8Z3KnaYtw9},
}

@inproceedings{neurips2025_rao_75986,
  title = {{Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=ENJhSqVkXH},
}

@inproceedings{neurips2025_symmetry_79004,
  title = {{Symmetry-Preserving Conformer Ensemble Networks for Molecular Representation Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=kgjY80e1LF},
}