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}

@online{allen_learning_2023,
  title = {Learning {{Together}}: {{Towards}} Foundational Models for Machine Learning Interatomic Potentials with Meta-Learning},
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  date = {2023-07-08},
  eprint = {2307.04012},
  eprinttype = {arXiv},
  eprintclass = {physics},
  url = {http://arxiv.org/abs/2307.04012},
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}

@misc{austin_nerscworkload_2018,
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}

@article{aykol_rational_2021,
  title = {Rational {{Solid-State Synthesis Routes}} for {{Inorganic Materials}}},
  author = {Aykol, Muratahan and Montoya, Joseph H. and Hummelshøj, Jens},
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@article{aykol_thermodynamic_2018,
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@online{barroso-luque_open_2024,
  title = {Open {{Materials}} 2024 ({{OMat24}}) {{Inorganic Materials Dataset}} and {{Models}}},
  author = {Barroso-Luque, Luis and Shuaibi, Muhammed and Fu, Xiang and Wood, Brandon M. and Dzamba, Misko and Gao, Meng and Rizvi, Ammar and Zitnick, C. Lawrence and Ulissi, Zachary W.},
  date = {2024-10-16},
  eprint = {2410.12771},
  eprinttype = {arXiv},
  eprintclass = {cond-mat, physics:physics},
  doi = {10.48550/arXiv.2410.12771},
  url = {http://arxiv.org/abs/2410.12771},
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}

@article{bartel_critical_2020,
  title = {A Critical Examination of Compound Stability Predictions from Machine-Learned Formation Energies},
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  date = {2020-07-10},
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}

@article{bartok_machine_2018,
  title = {Machine {{Learning}} a {{General-Purpose Interatomic Potential}} for {{Silicon}}},
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  organization = {arXiv.org},
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}

@online{batatia_mace_2023,
  title = {{{MACE}}: {{Higher Order Equivariant Message Passing Neural Networks}} for {{Fast}} and {{Accurate Force Fields}}},
  shorttitle = {{{MACE}}},
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  eprint = {2206.07697},
  eprinttype = {arXiv},
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  url = {http://arxiv.org/abs/2206.07697},
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  keywords = {Computer Science - Machine Learning,Condensed Matter - Materials Science,Physics - Chemical Physics,Statistics - Machine Learning}
}

@article{batzner_e3equivariant_2022,
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}

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}

@article{borg_quantifying_2023,
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}

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@article{cheetham_artificial_2024,
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@article{chen_graph_2019,
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@article{chen_universal_2022,
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}

@inproceedings{chiang_mlip_2025,
  title = {{{MLIP Arena}}: {{Advancing Fairness}} and {{Transparency}} in {{Machine Learning Interatomic Potentials}} through an {{Open}} and {{Accessible Benchmark Platform}}},
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}

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}

@article{choudhary_jarvisleaderboard_2024,
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}

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}

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}

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}

@article{davies_computational_2016,
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}

@article{demeester_evaluation_1975,
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@article{deng_chgnet_2023,
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@inproceedings{deng_imagenet_2009,
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  shorttitle = {{{ImageNet}}},
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}

@online{deng_overcoming_2024,
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  eprint = {2405.07105},
  eprinttype = {arXiv},
  eprintclass = {cond-mat},
  url = {https://arxiv.org/abs/2405.07105},
  urldate = {2024-05-14},
  pubstate = {prepublished},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Condensed Matter - Materials Science}
}

@article{deng_systematic_2025,
  title = {Systematic Softening in Universal Machine Learning Interatomic Potentials},
  author = {Deng, Bowen and Choi, Yunyeong and Zhong, Peichen and Riebesell, Janosh and Anand, Shashwat and Li, Zhuohan and Jun, KyuJung and Persson, Kristin A. and Ceder, Gerbrand},
  date = {2025-01-10},
  journaltitle = {npj Computational Materials},
  shortjournal = {npj Comput Mater},
  volume = {11},
  number = {1},
  pages = {1--9},
  publisher = {Nature Publishing Group},
  issn = {2057-3960},
  doi = {10.1038/s41524-024-01500-6},
  url = {https://www.nature.com/articles/s41524-024-01500-6},
  urldate = {2025-01-10},
  keywords = {Atomistic models,Computational methods}
}

@unpublished{depeweg_decomposition_2017,
  title = {Decomposition of {{Uncertainty}} in {{Bayesian Deep Learning}} for {{Efficient}} and {{Risk-sensitive Learning}}},
  author = {Depeweg, Stefan and Hernández-Lobato, José Miguel and Doshi-Velez, Finale and Udluft, Steffen},
  date = {2017-10-19},
  eprint = {1710.07283},
  eprinttype = {arXiv},
  eprintclass = {cs, stat},
  url = {http://arxiv.org/abs/1710.07283},
  urldate = {2019-03-03},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}
}

@inproceedings{depeweg_decomposition_2018,
  title = {Decomposition of {{Uncertainty}} in {{Bayesian Deep Learning}} for {{Efficient}} and {{Risk-sensitive Learning}}},
  booktitle = {Proceedings of the 35th {{International Conference}} on {{Machine Learning}}},
  author = {Depeweg, Stefan and Hernandez-Lobato, Jose-Miguel and Doshi-Velez, Finale and Udluft, Steffen},
  date = {2018-07-03},
  pages = {1184--1193},
  publisher = {PMLR},
  issn = {2640-3498},
  url = {https://proceedings.mlr.press/v80/depeweg18a.html},
  urldate = {2025-06-05},
  eventtitle = {International {{Conference}} on {{Machine Learning}}}
}

@article{deringer_generalpurpose_2020,
  title = {A General-Purpose Machine-Learning Force Field for Bulk and Nanostructured Phosphorus},
  author = {Deringer, Volker L. and Caro, Miguel A. and Csányi, Gábor},
  date = {2020-10-29},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  volume = {11},
  number = {1},
  pages = {5461},
  publisher = {Nature Publishing Group},
  issn = {2041-1723},
  doi = {10.1038/s41467-020-19168-z},
  url = {https://www.nature.com/articles/s41467-020-19168-z},
  urldate = {2023-02-08},
  issue = {1},
  keywords = {Materials chemistry,Structure of solids and liquids,Theory and computation}
}

@article{drautz_atomic_2019,
  title = {Atomic Cluster Expansion for Accurate and Transferable Interatomic Potentials},
  author = {Drautz, Ralf},
  date = {2019-01-08},
  journaltitle = {Physical Review B},
  shortjournal = {Phys. Rev. B},
  volume = {99},
  number = {1},
  pages = {014104},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevB.99.014104},
  url = {https://link.aps.org/doi/10.1103/PhysRevB.99.014104},
  urldate = {2023-07-27}
}

@article{draxl_nomad_2018,
  title = {{{NOMAD}}: {{The FAIR}} Concept for Big Data-Driven Materials Science},
  shorttitle = {{{NOMAD}}},
  author = {Draxl, Claudia and Scheffler, Matthias},
  date = {2018-09},
  journaltitle = {MRS Bulletin},
  volume = {43},
  number = {9},
  pages = {676--682},
  publisher = {Cambridge University Press},
  issn = {0883-7694, 1938-1425},
  doi = {10.1557/mrs.2018.208},
  url = {https://doi.org/10.1557/mrs.2018.208},
  urldate = {2020-07-29},
  keywords = {artificial intelligence,data repositories,data sharing,machine learning,metadata}
}

@article{dunn_benchmarking_2020,
  title = {Benchmarking Materials Property Prediction Methods: The {{Matbench}} Test Set and {{Automatminer}} Reference Algorithm},
  shorttitle = {Benchmarking Materials Property Prediction Methods},
  author = {Dunn, Alexander and Wang, Qi and Ganose, Alex and Dopp, Daniel and Jain, Anubhav},
  date = {2020-09-15},
  journaltitle = {npj Computational Materials},
  shortjournal = {npj Comput Mater},
  volume = {6},
  number = {1},
  pages = {1--10},
  publisher = {Nature Publishing Group},
  issn = {2057-3960},
  doi = {10.1038/s41524-020-00406-3},
  url = {https://www.nature.com/articles/s41524-020-00406-3},
  urldate = {2023-02-08},
  issue = {1},
  keywords = {Atomistic models,Computational methods}
}

@online{fu_forces_2023,
  title = {Forces Are Not {{Enough}}: {{Benchmark}} and {{Critical Evaluation}} for {{Machine Learning Force Fields}} with {{Molecular Simulations}}},
  shorttitle = {Forces Are Not {{Enough}}},
  author = {Fu, Xiang and Wu, Zhenghao and Wang, Wujie and Xie, Tian and Keten, Sinan and Gomez-Bombarelli, Rafael and Jaakkola, Tommi},
  date = {2023-08-26},
  eprint = {2210.07237},
  eprinttype = {arXiv},
  eprintclass = {physics},
  doi = {10.48550/arXiv.2210.07237},
  url = {http://arxiv.org/abs/2210.07237},
  urldate = {2025-05-12},
  pubstate = {prepublished},
  keywords = {Computer Science - Machine Learning,Physics - Chemical Physics,Physics - Computational Physics}
}

@online{fu_learning_2025,
  title = {Learning {{Smooth}} and {{Expressive Interatomic Potentials}} for {{Physical Property Prediction}}},
  author = {Fu, Xiang and Wood, Brandon M. and Barroso-Luque, Luis and Levine, Daniel S. and Gao, Meng and Dzamba, Misko and Zitnick, C. Lawrence},
  date = {2025-02-17},
  eprint = {2502.12147},
  eprinttype = {arXiv},
  eprintclass = {physics},
  doi = {10.48550/arXiv.2502.12147},
  url = {http://arxiv.org/abs/2502.12147},
  urldate = {2025-02-18},
  pubstate = {prepublished},
  keywords = {Computer Science - Machine Learning,Physics - Computational Physics}
}

@article{gibson_dataaugmentation_2022,
  title = {Data-Augmentation for Graph Neural Network Learning of the Relaxed Energies of Unrelaxed Structures},
  author = {Gibson, Jason and Hire, Ajinkya and Hennig, Richard G.},
  date = {2022-09-30},
  journaltitle = {npj Computational Materials},
  shortjournal = {npj Comput Mater},
  volume = {8},
  number = {1},
  pages = {1--7},
  publisher = {Nature Publishing Group},
  issn = {2057-3960},
  doi = {10.1038/s41524-022-00891-8},
  url = {https://www.nature.com/articles/s41524-022-00891-8},
  urldate = {2023-02-10},
  issue = {1},
  keywords = {Computational methods,Physics,Theoretical chemistry}
}

@unpublished{gilmer_neural_2017,
  title = {Neural {{Message Passing}} for {{Quantum Chemistry}}},
  author = {Gilmer, Justin and Schoenholz, Samuel S. and Riley, Patrick F. and Vinyals, Oriol and Dahl, George E.},
  date = {2017-06-12},
  eprint = {1704.01212},
  eprinttype = {arXiv},
  eprintclass = {cs},
  url = {http://arxiv.org/abs/1704.01212},
  urldate = {2020-08-03},
  keywords = {Computer Science - Machine Learning,I.2.6}
}

@article{glawe_optimal_2016,
  title = {The Optimal One Dimensional Periodic Table: A Modified {{Pettifor}} Chemical Scale from Data Mining},
  shorttitle = {The Optimal One Dimensional Periodic Table},
  author = {Glawe, Henning and Sanna, Antonio and Gross, E. K. U. and Marques, Miguel A. L.},
  date = {2016-09},
  journaltitle = {New Journal of Physics},
  shortjournal = {New J. Phys.},
  volume = {18},
  number = {9},
  pages = {093011},
  publisher = {IOP Publishing},
  issn = {1367-2630},
  doi = {10.1088/1367-2630/18/9/093011},
  url = {https://doi.org/10.1088/1367-2630/18/9/093011},
  urldate = {2022-02-09}
}

@software{goodall_aviary_2022,
  title = {Aviary: {{Machine}} Learning Models for Materials Discovery},
  shorttitle = {Aviary},
  author = {Goodall, Rhys and Riebesell, Janosh},
  date = {2022-06-11},
  doi = {10.5281/zenodo.6635011},
  url = {https://zenodo.org/records/6635011},
  urldate = {2025-05-12},
  organization = {Zenodo},
  version = {v0.0.3},
  keywords = {Materials Informatics Materials Discovery Machine Learning}
}

@article{goodall_predicting_2020,
  title = {Predicting Materials Properties without Crystal Structure: Deep Representation Learning from Stoichiometry},
  shorttitle = {Predicting Materials Properties without Crystal Structure},
  author = {Goodall, Rhys E. A. and Lee, Alpha A.},
  date = {2020-12-08},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  volume = {11},
  number = {1},
  pages = {6280},
  publisher = {Nature Publishing Group},
  issn = {2041-1723},
  doi = {10.1038/s41467-020-19964-7},
  url = {https://www.nature.com/articles/s41467-020-19964-7},
  urldate = {2023-02-08},
  issue = {1},
  keywords = {Computational methods,Materials science,Structure prediction}
}

@article{goodall_rapid_2022,
  title = {Rapid Discovery of Stable Materials by Coordinate-Free Coarse Graining},
  shorttitle = {Wren},
  author = {Goodall, Rhys E. A. and Parackal, Abhijith S. and Faber, Felix A. and Armiento, Rickard and Lee, Alpha A.},
  date = {2022-07-27},
  journaltitle = {Science Advances},
  volume = {8},
  number = {30},
  pages = {eabn4117},
  publisher = {American Association for the Advancement of Science},
  doi = {10.1126/sciadv.abn4117},
  url = {https://www.science.org/doi/10.1126/sciadv.abn4117},
  urldate = {2022-09-19}
}

@online{griffin_computational_2022,
  title = {Computational Needs of Quantum Mechanical Calculations of Materials for High-Energy Physics},
  author = {Griffin, Sinéad M.},
  date = {2022-05-22},
  eprint = {2205.10699},
  eprinttype = {arXiv},
  eprintclass = {hep-ex},
  doi = {10.48550/arXiv.2205.10699},
  url = {http://arxiv.org/abs/2205.10699},
  urldate = {2025-05-12},
  pubstate = {prepublished},
  keywords = {High Energy Physics - Experiment,High Energy Physics - Phenomenology}
}

@online{gruver_finetuned_2024,
  title = {Fine-{{Tuned Language Models Generate Stable Inorganic Materials}} as {{Text}}},
  author = {Gruver, Nate and Sriram, Anuroop and Madotto, Andrea and Wilson, Andrew Gordon and Zitnick, C. Lawrence and Ulissi, Zachary},
  date = {2024-02-06},
  eprint = {2402.04379},
  eprinttype = {arXiv},
  eprintclass = {cond-mat},
  url = {http://arxiv.org/abs/2402.04379},
  urldate = {2024-02-09},
  pubstate = {prepublished},
  keywords = {Computer Science - Machine Learning,Condensed Matter - Materials Science}
}

@article{hautier_accuracy_2012,
  title = {Accuracy of Density Functional Theory in Predicting Formation Energies of Ternary Oxides from Binary Oxides and Its Implication on Phase Stability},
  author = {Hautier, Geoffroy and Ong, Shyue Ping and Jain, Anubhav and Moore, Charles J. and Ceder, Gerbrand},
  date = {2012-04-30},
  journaltitle = {Physical Review B},
  shortjournal = {Phys. Rev. B},
  volume = {85},
  number = {15},
  pages = {155208},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevB.85.155208},
  url = {https://link.aps.org/doi/10.1103/PhysRevB.85.155208},
  urldate = {2023-01-17}
}

@article{hicks_aflow_2021,
  title = {The {{AFLOW Library}} of {{Crystallographic Prototypes}}: {{Part}} 3},
  shorttitle = {The {{AFLOW Library}} of {{Crystallographic Prototypes}}},
  author = {Hicks, David and Mehl, Michael J. and Esters, Marco and Oses, Corey and Levy, Ohad and Hart, Gus L. W. and Toher, Cormac and Curtarolo, Stefano},
  date = {2021-11-01},
  journaltitle = {Computational Materials Science},
  shortjournal = {Computational Materials Science},
  volume = {199},
  pages = {110450},
  issn = {0927-0256},
  doi = {10.1016/j.commatsci.2021.110450},
  url = {https://www.sciencedirect.com/science/article/pii/S0927025621001750},
  urldate = {2024-01-24},
  keywords = {Basis Vectors,Crystal Structure,Database,Lattice Vectors,Space Groups,Wyckoff Positions}
}

@article{horton_promises_2021,
  title = {Promises and Perils of Computational Materials Databases},
  author = {Horton, M. K. and Dwaraknath, S. and Persson, K. A.},
  date = {2021-01},
  journaltitle = {Nature Computational Science},
  shortjournal = {Nat Comput Sci},
  volume = {1},
  number = {1},
  pages = {3--5},
  publisher = {Nature Publishing Group},
  issn = {2662-8457},
  doi = {10.1038/s43588-020-00016-5},
  url = {https://www.nature.com/articles/s43588-020-00016-5},
  urldate = {2023-04-07},
  issue = {1},
  keywords = {Computational science,Materials science}
}

@article{ivanov_studies_1976,
  title = {Studies on the Mechanism of the Changes in Serum and Liver Gamma-Glutamyl Transpeptidase Activity. {{II}}. {{Experimental}} Hexachlorobenzene Porphyria in Rabbits},
  author = {Ivanov, E. and Krustev, L. and Adjarov, D. and Chernev, K. and Apostolov, I. and Dimitrov, P. and Drenska, E. and Stefanova, M. and Pramatarova, V.},
  date = {1976},
  journaltitle = {Enzyme},
  shortjournal = {Enzyme},
  volume = {21},
  number = {1},
  eprint = {233},
  eprinttype = {pubmed},
  pages = {8--20},
  issn = {0013-9432},
  doi = {10.1159/000458837},
  keywords = {Acetylglucosaminidase,Animals,gamma-Glutamyltransferase,Hexachlorobenzene,Liver,Mannosidases,Porphyrias,Porphyrins,Rabbits}
}

@article{jain_commentary_2013,
  title = {Commentary: {{The Materials Project}}: {{A}} Materials Genome Approach to Accelerating Materials Innovation},
  shorttitle = {Commentary},
  author = {Jain, Anubhav and Ong, Shyue Ping and Hautier, Geoffroy and Chen, Wei and Richards, William Davidson and Dacek, Stephen and Cholia, Shreyas and Gunter, Dan and Skinner, David and Ceder, Gerbrand and Persson, Kristin A.},
  date = {2013-07},
  journaltitle = {APL Materials},
  volume = {1},
  number = {1},
  pages = {011002},
  publisher = {American Institute of Physics},
  doi = {10.1063/1.4812323},
  url = {https://doi.org/10.1063/1.4812323},
  urldate = {2022-02-21}
}

@online{kovacs_evaluation_2023,
  title = {Evaluation of the {{MACE Force Field Architecture}}: From {{Medicinal Chemistry}} to {{Materials Science}}},
  shorttitle = {Evaluation of the {{MACE Force Field Architecture}}},
  author = {Kovacs, David Peter and Batatia, Ilyes and Arany, Eszter Sara and Csanyi, Gabor},
  date = {2023-05-23},
  eprint = {2305.14247},
  eprinttype = {arXiv},
  eprintclass = {physics, stat},
  url = {http://arxiv.org/abs/2305.14247},
  urldate = {2023-05-27},
  pubstate = {prepublished},
  keywords = {Physics - Chemical Physics,Statistics - Machine Learning}
}

@article{kovacs_quantitative_2021,
  title = {Quantitative Interpretation Explains Machine Learning Models for Chemical Reaction Prediction and Uncovers Bias},
  author = {Kovács, Dávid Péter and McCorkindale, William and Lee, Alpha A.},
  date = {2021-03-16},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  volume = {12},
  number = {1},
  pages = {1695},
  publisher = {Nature Publishing Group},
  issn = {2041-1723},
  doi = {10.1038/s41467-021-21895-w},
  url = {https://www.nature.com/articles/s41467-021-21895-w},
  urldate = {2023-07-04},
  issue = {1},
  keywords = {Automation,Cheminformatics,Computational chemistry,Computational science}
}

@article{kpanou_robustness_2021,
  title = {On the Robustness of Generalization of Drug–Drug Interaction Models},
  author = {Kpanou, Rogia and Osseni, Mazid Abiodoun and Tossou, Prudencio and Laviolette, Francois and Corbeil, Jacques},
  date = {2021-10-04},
  journaltitle = {BMC Bioinformatics},
  shortjournal = {BMC Bioinformatics},
  volume = {22},
  eprint = {34607569},
  eprinttype = {pubmed},
  pages = {477},
  issn = {1471-2105},
  doi = {10.1186/s12859-021-04398-9},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8489092/},
  urldate = {2023-07-04},
  pmcid = {PMC8489092}
}

@article{lan_adsorbml_2023,
  title = {{{AdsorbML}}: A Leap in Efficiency for Adsorption Energy Calculations Using Generalizable Machine Learning Potentials},
  shorttitle = {{{AdsorbML}}},
  author = {Lan, Janice and Palizhati, Aini and Shuaibi, Muhammed and Wood, Brandon M. and Wander, Brook and Das, Abhishek and Uyttendaele, Matt and Zitnick, C. Lawrence and Ulissi, Zachary W.},
  date = {2023-09-22},
  journaltitle = {npj Computational Materials},
  shortjournal = {npj Comput Mater},
  volume = {9},
  number = {1},
  pages = {1--9},
  publisher = {Nature Publishing Group},
  issn = {2057-3960},
  doi = {10.1038/s41524-023-01121-5},
  url = {https://www.nature.com/articles/s41524-023-01121-5},
  urldate = {2025-06-05},
  keywords = {Computational methods,Electrocatalysis}
}

@online{lan_adsorbml_2023_arxiv,
  title = {{{AdsorbML}}: {{Accelerating Adsorption Energy Calculations}} with {{Machine Learning}}},
  shorttitle = {{{AdsorbML}}},
  author = {Lan, Janice and Palizhati, Aini and Shuaibi, Muhammed and Wood, Brandon M. and Wander, Brook and Das, Abhishek and Uyttendaele, Matt and Zitnick, C. Lawrence and Ulissi, Zachary W.},
  date = {2023-01-04},
  eprint = {2211.16486},
  eprinttype = {arXiv},
  eprintclass = {cond-mat},
  url = {http://arxiv.org/abs/2211.16486},
  urldate = {2023-08-07},
  pubstate = {prepublished},
  keywords = {Computer Science - Machine Learning,Condensed Matter - Materials Science}
}

@inproceedings{lee_foundation_2023,
  title = {Towards {{Foundation Models}} for {{Materials Science}}: {{The Open MatSci ML Toolkit}}},
  shorttitle = {Towards {{Foundation Models}} for {{Materials Science}}},
  booktitle = {Proceedings of the {{SC}} '23 {{Workshops}} of the {{International Conference}} on {{High Performance Computing}}, {{Network}}, {{Storage}}, and {{Analysis}}},
  author = {Lee, Kin Long Kelvin and Gonzales, Carmelo and Spellings, Matthew and Galkin, Mikhail and Miret, Santiago and Kumar, Nalini},
  date = {2023-11-12},
  eprint = {2310.07864},
  eprinttype = {arXiv},
  eprintclass = {cond-mat},
  pages = {51--59},
  doi = {10.1145/3624062.3626081},
  url = {http://arxiv.org/abs/2310.07864},
  urldate = {2025-05-12},
  keywords = {Condensed Matter - Materials Science,Physics - Computational Physics}
}

@article{li_critical_2023,
  title = {A Critical Examination of Robustness and Generalizability of Machine Learning Prediction of Materials Properties},
  author = {Li, Kangming and DeCost, Brian and Choudhary, Kamal and Greenwood, Michael and Hattrick-Simpers, Jason},
  date = {2023-04-07},
  journaltitle = {npj Computational Materials},
  shortjournal = {npj Comput Mater},
  volume = {9},
  number = {1},
  eprint = {2210.13597},
  eprinttype = {arXiv},
  eprintclass = {cond-mat},
  pages = {55},
  issn = {2057-3960},
  doi = {10.1038/s41524-023-01012-9},
  url = {http://arxiv.org/abs/2210.13597},
  urldate = {2023-06-18},
  keywords = {Condensed Matter - Materials Science}
}

@article{li_exploiting_2023,
  title = {Exploiting Redundancy in Large Materials Datasets for Efficient Machine Learning with Less Data},
  author = {Li, Kangming and Persaud, Daniel and Choudhary, Kamal and DeCost, Brian and Greenwood, Michael and Hattrick-Simpers, Jason},
  date = {2023-11-10},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  volume = {14},
  number = {1},
  pages = {7283},
  publisher = {Nature Publishing Group},
  issn = {2041-1723},
  doi = {10.1038/s41467-023-42992-y},
  url = {https://www.nature.com/articles/s41467-023-42992-y},
  urldate = {2023-12-14},
  issue = {1},
  keywords = {Condensed-matter physics,Materials chemistry,Scaling laws,Scientific data}
}

@online{liao_equiformer_2023,
  title = {Equiformer: {{Equivariant Graph Attention Transformer}} for {{3D Atomistic Graphs}}},
  shorttitle = {Equiformer},
  author = {Liao, Yi-Lun and Smidt, Tess},
  date = {2023-02-28},
  eprint = {2206.11990},
  eprinttype = {arXiv},
  eprintclass = {cs},
  doi = {10.48550/arXiv.2206.11990},
  url = {http://arxiv.org/abs/2206.11990},
  urldate = {2025-05-12},
  pubstate = {prepublished},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Physics - Computational Physics}
}

@online{liao_equiformerv2_2024,
  title = {{{EquiformerV2}}: {{Improved Equivariant Transformer}} for {{Scaling}} to {{Higher-Degree Representations}}},
  shorttitle = {{{EquiformerV2}}},
  author = {Liao, Yi-Lun and Wood, Brandon and Das, Abhishek and Smidt, Tess},
  date = {2024-03-06},
  eprint = {2306.12059},
  eprinttype = {arXiv},
  eprintclass = {physics},
  doi = {10.48550/arXiv.2306.12059},
  url = {http://arxiv.org/abs/2306.12059},
  urldate = {2024-07-25},
  pubstate = {prepublished},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Physics - Computational Physics}
}

@online{liao_generalizing_2024,
  title = {Generalizing {{Denoising}} to {{Non-Equilibrium Structures Improves Equivariant Force Fields}}},
  author = {Liao, Yi-Lun and Smidt, Tess and Shuaibi, Muhammed and Das, Abhishek},
  date = {2024-12-19},
  eprint = {2403.09549},
  eprinttype = {arXiv},
  eprintclass = {cs},
  doi = {10.48550/arXiv.2403.09549},
  url = {http://arxiv.org/abs/2403.09549},
  urldate = {2025-05-12},
  pubstate = {prepublished},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Physics - Chemical Physics,Physics - Computational Physics}
}

@article{makar_formate_1975,
  title = {Formate Assay in Body Fluids: Application in Methanol Poisoning},
  shorttitle = {Formate Assay in Body Fluids},
  author = {Makar, A. B. and McMartin, K. E. and Palese, M. and Tephly, T. R.},
  date = {1975-06},
  journaltitle = {Biochemical Medicine},
  shortjournal = {Biochem Med},
  volume = {13},
  number = {2},
  eprint = {1},
  eprinttype = {pubmed},
  pages = {117--126},
  issn = {0006-2944},
  doi = {10.1016/0006-2944(75)90147-7},
  keywords = {Aldehyde Oxidoreductases,Animals,Body Fluids,Carbon Dioxide,Formates,Haplorhini,Humans,Hydrogen-Ion Concentration,Kinetics,Methanol,Methods,Pseudomonas}
}

@article{mcdermott_graphbased_2021,
  title = {A Graph-Based Network for Predicting Chemical Reaction Pathways in Solid-State Materials Synthesis},
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  date = {2021-05-25},
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  urldate = {2022-04-23},
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  keywords = {Computational methods,Solid-phase synthesis,Thermodynamics}
}

@article{merchant_scaling_2023,
  title = {Scaling Deep Learning for Materials Discovery},
  author = {Merchant, Amil and Batzner, Simon and Schoenholz, Samuel S. and Aykol, Muratahan and Cheon, Gowoon and Cubuk, Ekin Dogus},
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  journaltitle = {Nature},
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  publisher = {Nature Publishing Group},
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  url = {https://www.nature.com/articles/s41586-023-06735-9},
  urldate = {2023-11-29},
  keywords = {Computer science,Scaling laws}
}

@article{meredig_can_2018,
  title = {Can Machine Learning Identify the next High-Temperature Superconductor? {{Examining}} Extrapolation Performance for Materials Discovery},
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}

@article{mier_lysosomal_1975,
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}

@online{miret_open_2022,
  title = {The {{Open MatSci ML Toolkit}}: {{A Flexible Framework}} for {{Machine Learning}} in {{Materials Science}}},
  shorttitle = {The {{Open MatSci ML Toolkit}}},
  author = {Miret, Santiago and Lee, Kin Long Kelvin and Gonzales, Carmelo and Nassar, Marcel and Spellings, Matthew},
  date = {2022-10-31},
  eprint = {2210.17484},
  eprinttype = {arXiv},
  eprintclass = {cs},
  doi = {10.48550/arXiv.2210.17484},
  url = {http://arxiv.org/abs/2210.17484},
  urldate = {2025-05-12},
  pubstate = {prepublished},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Condensed Matter - Materials Science}
}

@report{mok_directionbased_2022,
  title = {Direction-Based {{Graph Representation}} to {{Accelerate Stable Catalysts Discovery}}},
  author = {Mok, Dong Hyeon and Kim, Jongseung and Back, Seoin},
  date = {2022-09-30},
  institution = {Chemistry},
  doi = {10.26434/chemrxiv-2022-dp58c},
  url = {https://doi.org/10.26434/chemrxiv-2022-dp58c},
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}

@online{montanari_goldilocks_2024,
  title = {Goldilocks Convergence Tools and Best Practices for Numerical Approximations in {{Density Functional Theory}} Calculations},
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}

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  title = {Orb: {{A Fast}}, {{Scalable Neural Network Potential}}},
  shorttitle = {Orb},
  author = {Neumann, Mark and Gin, James and Rhodes, Benjamin and Bennett, Steven and Li, Zhiyi and Choubisa, Hitarth and Hussey, Arthur and Godwin, Jonathan},
  date = {2024-10-29},
  eprint = {2410.22570},
  eprinttype = {arXiv},
  doi = {10.48550/arXiv.2410.22570},
  url = {http://arxiv.org/abs/2410.22570},
  urldate = {2024-10-31},
  pubstate = {prepublished},
  keywords = {Computer Science - Machine Learning,Condensed Matter - Materials Science}
}

@article{orskov_effects_1975,
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}

@article{parackal_identifying_2024,
  title = {Identifying Crystal Structures beyond Known Prototypes from X-Ray Powder Diffraction Spectra},
  author = {Parackal, Abhijith S. and Goodall, Rhys E. A. and Faber, Felix A. and Armiento, Rickard},
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  publisher = {American Physical Society},
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}

@article{park_developing_2020,
  title = {Developing an Improved Crystal Graph Convolutional Neural Network Framework for Accelerated Materials Discovery},
  author = {Park, Cheol Woo and Wolverton, Chris},
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  publisher = {American Physical Society},
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}

@article{park_scalable_2024,
  title = {Scalable {{Parallel Algorithm}} for {{Graph Neural Network Interatomic Potentials}} in {{Molecular Dynamics Simulations}}},
  author = {Park, Yutack and Kim, Jaesun and Hwang, Seungwoo and Han, Seungwu},
  date = {2024-06-11},
  journaltitle = {Journal of Chemical Theory and Computation},
  shortjournal = {J. Chem. Theory Comput.},
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}

@online{passaro_reducing_2023,
  title = {Reducing {{SO}}(3) {{Convolutions}} to {{SO}}(2) for {{Efficient Equivariant GNNs}}},
  author = {Passaro, Saro and Zitnick, C. Lawrence},
  date = {2023-06-14},
  eprint = {2302.03655},
  eprinttype = {arXiv},
  eprintclass = {physics},
  doi = {10.48550/arXiv.2302.03655},
  url = {http://arxiv.org/abs/2302.03655},
  urldate = {2024-03-27},
  pubstate = {prepublished},
  keywords = {20C35 (Primary),Computer Science - Machine Learning,I.2.6,J.2,Physics - Chemical Physics,Physics - Computational Physics}
}

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}

@online{pota_thermal_2024,
  title = {Thermal {{Conductivity Predictions}} with {{Foundation Atomistic Models}}},
  author = {Póta, Balázs and Ahlawat, Paramvir and Csányi, Gábor and Simoncelli, Michele},
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  eprinttype = {arXiv},
  eprintclass = {cond-mat, physics:physics},
  url = {http://arxiv.org/abs/2408.00755},
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  pubstate = {prepublished},
  keywords = {Condensed Matter - Materials Science,Physics - Applied Physics}
}

@online{qi_robust_2023,
  title = {Robust {{Training}} of {{Machine Learning Interatomic Potentials}} with {{Dimensionality Reduction}} and {{Stratified Sampling}}},
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  date = {2023-07-24},
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  eprinttype = {arXiv},
  eprintclass = {cond-mat},
  doi = {10.48550/arXiv.2307.13710},
  url = {http://arxiv.org/abs/2307.13710},
  urldate = {2023-07-27},
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  keywords = {Condensed Matter - Materials Science}
}

@article{raccuglia_machinelearningassisted_2016,
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  keywords = {Computer science,Solid-state chemistry,Theory and computation}
}

@online{rhodes_orbv3_2025,
  title = {Orb-v3: Atomistic Simulation at Scale},
  shorttitle = {Orb-V3},
  author = {Rhodes, Benjamin and Vandenhaute, Sander and Šimkus, Vaidotas and Gin, James and Godwin, Jonathan and Duignan, Tim and Neumann, Mark},
  date = {2025-04-08},
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  eprinttype = {arXiv},
  eprintclass = {cond-mat},
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}

@article{riebesell_machine_2024,
  title = {Towards {{Machine Learning Foundation Models}} for {{Materials Chemistry}}},
  author = {Riebesell, Janosh},
  date = {2024-11-11T16:06:07Z},
  doi = {10.17863/CAM.113233},
  url = {https://www.repository.cam.ac.uk/handle/1810/375689},
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}

@article{riebesell_matbenchdata_2023,
  title = {Matbench Discovery - Data Files},
  author = {Riebesell, Janosh},
  date = {2023-04},
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}

@software{riebesell_matbenchzenodo_2024,
  title = {Janosh/Matbench-Discovery: V1.3.1},
  author = {Riebesell, Janosh and Goodall, Rhys and Chiang, Yuan (Cyrus) and Benner, Philipp and Park, Yutack and Neumann, Mark and {yanghan-microsoft}},
  date = {2024-09},
  doi = {10.5281/zenodo.13750664},
  url = {https://doi.org/10.5281/zenodo.13750664},
  organization = {Zenodo},
  version = {v1.3.1}
}

@online{riebesell_pushing_2024,
  title = {Pushing the {{Pareto}} Front of Band Gap and Permittivity: {{ML-guided}} Search for Dielectric Materials},
  shorttitle = {Pushing the {{Pareto}} Front of Band Gap and Permittivity},
  author = {Riebesell, Janosh and Surta, T. Wesley and Goodall, Rhys and Gaultois, Michael and Lee, Alpha A.},
  date = {2024-01-11},
  url = {https://arxiv.org/abs/2401.05848v1},
  urldate = {2024-01-12},
  organization = {arXiv.org}
}

@software{riebesell_pymatviz_2022,
  title = {Pymatviz: {{A}} Toolkit for Visualizations in Materials Informatics},
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  urldate = {2023-12-02},
  version = {0.8.0}
}

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}

@article{schmidt_dataset_2022,
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  author = {Schmidt, Jonathan and Wang, Hai-Chen and Cerqueira, Tiago F. T. and Botti, Silvana and Marques, Miguel A. L.},
  date = {2022-03-02},
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  publisher = {Nature Publishing Group},
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  urldate = {2023-09-16},
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  keywords = {Condensed-matter physics,Theory and computation}
}

@article{schmidt_improving_2024,
  title = {Improving Machine-Learning Models in Materials Science through Large Datasets},
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}

@online{schmidt_largescale_2022,
  title = {Large-Scale Machine-Learning-Assisted Exploration of the Whole Materials Space},
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  date = {2022-10-02},
  eprint = {2210.00579},
  eprinttype = {arXiv},
  eprintclass = {cond-mat, physics:physics},
  url = {http://arxiv.org/abs/2210.00579},
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  pubstate = {prepublished},
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}

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}

@online{shoghi_molecules_2023,
  title = {From {{Molecules}} to {{Materials}}: {{Pre-training Large Generalizable Models}} for {{Atomic Property Prediction}}},
  shorttitle = {From {{Molecules}} to {{Materials}}},
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  eprinttype = {arXiv},
  eprintclass = {cs},
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}

@online{shuang_universal_2025,
  title = {Universal Machine Learning Interatomic Potentials Poised to Supplant {{DFT}} in Modeling General Defects in Metals and Random Alloys},
  author = {Shuang, Fei and Wei, Zixiong and Liu, Kai and Gao, Wei and Dey, Poulumi},
  date = {2025-02-05},
  eprint = {2502.03578},
  eprinttype = {arXiv},
  eprintclass = {cond-mat},
  doi = {10.48550/arXiv.2502.03578},
  url = {http://arxiv.org/abs/2502.03578},
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  pubstate = {prepublished},
  keywords = {Condensed Matter - Materials Science,Physics - Computational Physics}
}

@online{sriram_open_2023,
  title = {The {{Open DAC}} 2023 {{Dataset}} and {{Challenges}} for {{Sorbent Discovery}} in {{Direct Air Capture}}},
  author = {Sriram, Anuroop and Choi, Sihoon and Yu, Xiaohan and Brabson, Logan M. and Das, Abhishek and Ulissi, Zachary and Uyttendaele, Matt and Medford, Andrew J. and Sholl, David S.},
  date = {2023-11-01},
  eprint = {2311.00341},
  eprinttype = {arXiv},
  eprintclass = {cond-mat},
  doi = {10.48550/arXiv.2311.00341},
  url = {http://arxiv.org/abs/2311.00341},
  urldate = {2023-11-02},
  pubstate = {prepublished},
  keywords = {Computer Science - Machine Learning,Condensed Matter - Materials Science}
}

@article{sun_thermodynamic_2016,
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}

@article{takamoto_teanet_2022,
  title = {{{TeaNet}}: {{Universal}} Neural Network Interatomic Potential Inspired by Iterative Electronic Relaxations},
  shorttitle = {{{TeaNet}}},
  author = {Takamoto, So and Izumi, Satoshi and Li, Ju},
  date = {2022-05-01},
  journaltitle = {Computational Materials Science},
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  url = {https://www.sciencedirect.com/science/article/pii/S0927025622000799},
  urldate = {2023-05-24},
  keywords = {Graph neural network,Interatomic potential,Molecular dynamics,Neural Network Potential}
}

@article{takamoto_universal_2022,
  title = {Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements},
  author = {Takamoto, So and Shinagawa, Chikashi and Motoki, Daisuke and Nakago, Kosuke and Li, Wenwen and Kurata, Iori and Watanabe, Taku and Yayama, Yoshihiro and Iriguchi, Hiroki and Asano, Yusuke and Onodera, Tasuku and Ishii, Takafumi and Kudo, Takao and Ono, Hideki and Sawada, Ryohto and Ishitani, Ryuichiro and Ong, Marc and Yamaguchi, Taiki and Kataoka, Toshiki and Hayashi, Akihide and Charoenphakdee, Nontawat and Ibuka, Takeshi},
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  journaltitle = {Nature Communications},
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}

@online{thomas_tensor_2018,
  title = {Tensor Field Networks: {{Rotation-}} and Translation-Equivariant Neural Networks for {{3D}} Point Clouds},
  shorttitle = {Tensor Field Networks},
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  date = {2018-05-18},
  eprint = {1802.08219},
  eprinttype = {arXiv},
  eprintclass = {cs},
  doi = {10.48550/arXiv.1802.08219},
  url = {http://arxiv.org/abs/1802.08219},
  urldate = {2023-07-23},
  pubstate = {prepublished},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,Computer Science - Neural and Evolutionary Computing}
}

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  title = {The {{Open Catalyst}} 2022 ({{OC22}}) {{Dataset}} and {{Challenges}} for {{Oxide Electrocatalysts}}},
  author = {Tran, Richard and Lan, Janice and Shuaibi, Muhammed and Wood, Brandon M. and Goyal, Siddharth and Das, Abhishek and Heras-Domingo, Javier and Kolluru, Adeesh and Rizvi, Ammar and Shoghi, Nima and Sriram, Anuroop and Therrien, Felix and Abed, Jehad and Voznyy, Oleksandr and Sargent, Edward H. and Ulissi, Zachary and Zitnick, C. Lawrence},
  date = {2022-11-04},
  eprint = {2206.08917},
  eprinttype = {arXiv},
  eprintclass = {cond-mat, physics:physics},
  doi = {10.48550/arXiv.2206.08917},
  url = {http://arxiv.org/abs/2206.08917},
  urldate = {2023-02-08},
  pubstate = {prepublished},
  keywords = {Computer Science - Machine Learning,Condensed Matter - Materials Science,Physics - Computational Physics}
}

@article{tran_open_2023,
  title = {The {{Open Catalyst}} 2022 ({{OC22}}) {{Dataset}} and {{Challenges}} for {{Oxide Electrocatalysts}}},
  author = {Tran, Richard and Lan, Janice and Shuaibi, Muhammed and Wood, Brandon M. and Goyal, Siddharth and Das, Abhishek and Heras-Domingo, Javier and Kolluru, Adeesh and Rizvi, Ammar and Shoghi, Nima and Sriram, Anuroop and Therrien, Félix and Abed, Jehad and Voznyy, Oleksandr and Sargent, Edward H. and Ulissi, Zachary and Zitnick, C. Lawrence},
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  publisher = {American Chemical Society},
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}

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}

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  title = {Attention {{Is All You Need}}},
  author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
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  eprintclass = {cs},
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  urldate = {2020-08-05},
  keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning}
}

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  date = {2020-09-29},
  journaltitle = {Nature Communications},
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}

@article{wang_framework_2021,
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  keywords = {Computational methods,Density functional theory,Electronic structure,Materials science,Theory and computation}
}

@article{wang_predicting_2021,
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}

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@article{ward_including_2017,
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}

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  keywords = {Materials Science and Engineering}
}

@article{ward_matminer_2018,
  title = {Matminer: {{An}} Open Source Toolkit for Materials Data Mining},
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  urldate = {2023-12-06},
  keywords = {Data mining,Machine learning,Materials informatics,Open source software}
}

@article{wen_chemical_2023,
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  journaltitle = {Nature Computational Science},
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}

@article{wu_moleculenet_2018,
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}

@article{xie_crystal_2018,
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}

@online{yang_mattersim_2024,
  title = {{{MatterSim}}: {{A Deep Learning Atomistic Model Across Elements}}, {{Temperatures}} and {{Pressures}}},
  shorttitle = {{{MatterSim}}},
  author = {Yang, Han and Hu, Chenxi and Zhou, Yichi and Liu, Xixian and Shi, Yu and Li, Jielan and Li, Guanzhi and Chen, Zekun and Chen, Shuizhou and Zeni, Claudio and Horton, Matthew and Pinsler, Robert and Fowler, Andrew and Zügner, Daniel and Xie, Tian and Smith, Jake and Sun, Lixin and Wang, Qian and Kong, Lingyu and Liu, Chang and Hao, Hongxia and Lu, Ziheng},
  date = {2024-05-08},
  eprint = {2405.04967},
  eprinttype = {arXiv},
  eprintclass = {cond-mat},
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  urldate = {2024-05-09},
  pubstate = {prepublished},
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}

@article{yuan_analytical_2024,
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  urldate = {2025-05-12},
  keywords = {Computational science,Theoretical chemistry}
}

@article{zahrt_cautionary_2020,
  title = {Cautionary {{Guidelines}} for {{Machine Learning Studies}} with {{Combinatorial Datasets}}},
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  keywords = {enantioselective catalysis,machine learning}
}

@article{zhu_fast_2023,
  title = {Fast Uncertainty Estimates in Deep Learning Interatomic Potentials},
  author = {Zhu, Albert and Batzner, Simon and Musaelian, Albert and Kozinsky, Boris},
  date = {2023-04-27},
  journaltitle = {The Journal of Chemical Physics},
  shortjournal = {The Journal of Chemical Physics},
  volume = {158},
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  issn = {0021-9606},
  doi = {10.1063/5.0136574},
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}

@article{zuo_accelerating_2021,
  title = {Accelerating Materials Discovery with {{Bayesian}} Optimization and Graph Deep Learning},
  shorttitle = {{{BOWSR}}},
  author = {Zuo, Yunxing and Qin, Mingde and Chen, Chi and Ye, Weike and Li, Xiangguo and Luo, Jian and Ong, Shyue Ping},
  date = {2021-10-01},
  journaltitle = {Materials Today},
  shortjournal = {Materials Today},
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  doi = {10.1016/j.mattod.2021.08.012},
  url = {https://www.sciencedirect.com/science/article/pii/S1369702121002984},
  urldate = {2021-10-21},
  keywords = {Bayesian optimization,Deep learning,Graph neural network,Materials discovery}
}
