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

@inproceedings{aaai2023_foundation_3720,
  title = {{Foundation Model for Material Science}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26793},
}

@inproceedings{iclr2024_domain_33794,
  title = {{Domain-Agnostic Molecular Generation with Chemical Feedback}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19281},
}

@inproceedings{aaai2024_generating_6124,
  title = {{Generating Novel Leads for Drug Discovery Using LLMs with Logical Feedback}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27751},
}

@inproceedings{icml2024_llm_45081,
  title = {{LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33371},
}

@inproceedings{icml2024_structured_43569,
  title = {{Structured Chemistry Reasoning with Large Language Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34883},
}

@inproceedings{icml2024_chemreasoner_43407,
  title = {{CHEMREASONER: Heuristic Search over a Large Language Model’s Knowledge Space using Quantum-Chemical Feedback}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35045},
}

@inproceedings{iclr2025_efficient_82662,
  title = {{Efficient Evolutionary Search Over Chemical Space with Large Language Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29136},
}

@inproceedings{iclr2025_llm_83326,
  title = {{LLM-SR: Scientific Equation Discovery via Programming with Large Language Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28487},
}

@inproceedings{neurips2025_bioreason_79146,
  title = {{BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=mDjEKAwJOF},
}

@inproceedings{iclr2025_moose_82475,
  title = {{MOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29319},
}

@inproceedings{aaai2024_imitation_6277,
  title = {{Imitation of Life: A Search Engine for Biologically Inspired Design}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27805},
}

@inproceedings{neurips2024_sciinstruct_71666,
  title = {{SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97744},
}

@inproceedings{neurips2024_can_74184,
  title = {{Can LLMs Solve Molecule Puzzles? A Multimodal Benchmark for Molecular Structure Elucidation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97472},
}

@inproceedings{neurips2025_atmossci_80012,
  title = {{AtmosSci-Bench: Evaluating the Recent Advance of Large Language Model for Atmospheric Science}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=vDm8VWYYuX},
}

@inproceedings{icml2024_entropy_43901,
  title = {{Entropy-Reinforced Planning with Large Language Models for Drug Discovery}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34551},
}

@inproceedings{icml2025_llm_47098,
  title = {{LLM-Augmented Chemical Synthesis and Design Decision Programs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45488},
}

@inproceedings{neurips2021_mind_63494,
  title = {{MIND dataset for diet planning and dietary healthcare with machine learning: Dataset creation using combinatorial optimization and controllable generation with domain experts}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://openreview.net/forum?id=woX9uagUQiE},
}

@inproceedings{neurips2024_novobench_72168,
  title = {{NovoBench: Benchmarking Deep Learning-based emphDe Novo Sequencing Methods in Proteomics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97686},
}

@inproceedings{neurips2025_chempile_79055,
  title = {{ChemPile: A 250 GB Diverse and Curated Dataset for Chemical Foundation Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=l8Mz3dcK1S},
}

@inproceedings{iclr2025_discoverybench_83944,
  title = {{DiscoveryBench: Towards Data-Driven Discovery with Large Language Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/32050},
}

@inproceedings{iclr2026_vcworld_87982,
  title = {{VCWorld: A Biological World Model for Virtual Cell Simulation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=hhq89Hs7T3},
}

@inproceedings{icml2025_generalists_49139,
  title = {{Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43486},
}