% Key papers for Machine-learned interatomic potentials (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2017_schnet_56658,
  title = {{SchNet: A continuous-filter convolutional neural network for modeling quantum interactions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8893},
}

@inproceedings{neurips2018_end_57096,
  title = {{End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11438},
}

@inproceedings{aaai2019_molecular_100188,
  title = {{Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3896},
}

@inproceedings{neurips2019_cormorant_58056,
  title = {{Cormorant: Covariant Molecular Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14627},
}

@inproceedings{iclr2022_crystal_30792,
  title = {{Crystal Diffusion Variational Autoencoder for Periodic Material Generation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/7063},
}

@inproceedings{icml2025_learning_47287,
  title = {{Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45302},
}

@inproceedings{neurips2022_so3krates_66235,
  title = {{So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53570},
}

@inproceedings{icml2021_non_39668,
  title = {{Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9427},
}

@inproceedings{neurips2021_deep_62355,
  title = {{Deep Molecular Representation Learning via Fusing Physical and Chemical Information}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27882},
}

@inproceedings{neurips2025_boom_77125,
  title = {{BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=QoBxQrvFRd},
}

@inproceedings{neurips2022_gold_65955,
  title = {{Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54906},
}

@inproceedings{neurips2025_mlip_77279,
  title = {{MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials via an Open, Accessible Benchmark Platform}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=SAT0KPA5UO},
}

@inproceedings{iclr2019_a_29133,
  title = {{A Generative Model For Electron Paths}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1083},
}

@inproceedings{iclr2022_ab_31453,
  title = {{Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/7108},
}

@inproceedings{iclr2023_sampling_32649,
  title = {{Sampling-free Inference for Ab-Initio Potential Energy Surface Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11217},
}

@inproceedings{icml2023_differentiable_41399,
  title = {{Differentiable Simulations for Enhanced Sampling of Rare Events}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24708},
}

@inproceedings{icml2025_the_47129,
  title = {{The dark side of the forces: assessing non-conservative force models for atomistic machine learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45458},
}

@inproceedings{neurips2024_a_72898,
  title = {{A Recipe for Charge Density Prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94512},
}

@inproceedings{neurips2023_qh9_66921,
  title = {{QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/73689},
}

@inproceedings{neurips2023_m_68890,
  title = {{M\$^2\$Hub: Unlocking the Potential of Machine Learning for Materials Discovery}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/73520},
}

@inproceedings{aaai2026_revisiting_99883,
  title = {{Revisiting the Canonicalization for Fast and Accurate Crystal Tensor Property Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37004},
}

@inproceedings{neurips2024_nabla_71222,
  title = {{\$nabla^2\$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97788},
}

@inproceedings{neurips2024_neural_71415,
  title = {{Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95837},
}

@inproceedings{neurips2025_mof_79437,
  title = {{MOF-BFN: Metal-Organic Frameworks Structure Prediction via Bayesian Flow Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=pNwiFucAtA},
}

@inproceedings{iclr2026_learning_86401,
  title = {{Learning from the Electronic Structure of Molecules across the Periodic Table}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=PS1YS8Wv4t},
}

@inproceedings{icml2025_physics_47986,
  title = {{Physics-Informed Weakly Supervised Learning For Interatomic Potentials}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44619},
}

@inproceedings{icml2026_dissolvr_52234,
  title = {{DISSOLVR: An Interpretable and Fast Framework for Aqueous and Organic Solubility Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=TwptiGypFR},
}

@inproceedings{neurips2025_chemixhub_75453,
  title = {{CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=8HUnx0rJNq},
}

@inproceedings{neurips2025_all_78827,
  title = {{All that structure matches does not glitter}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=ig9ujp50D4},
}

@inproceedings{aaai2026_quantum_107055,
  title = {{Quantum Transformer for Molecular Learning: Multi-Configuration Ground-State Energy Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39407},
}

@inproceedings{aaai2026_cyclechemist_108872,
  title = {{CycleChemist: A Dual-Pronged Machine Learning Framework for Organic Photovoltaic Discovery}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/41224},
}

@inproceedings{iclr2024_equivariant_33853,
  title = {{Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19222},
}

@inproceedings{icml2026_teaching_51162,
  title = {{Teaching Molecular Dynamics to a Non-Autoregressive Ionic Transport Predictor}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=JJ3KlnhGJJ},
}

@inproceedings{icml2026_card_51289,
  title = {{CARD: Coarse-to-fine Autoregressive Modeling with Radix-based Decomposition for Transferable Free Energy Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Kdc1UvnMKk},
}

@inproceedings{icml2026_latent_51808,
  title = {{Latent Diffusion Pretraining for Crystal Property Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Q5uj05ynu5},
}