{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-open-molecules-2025-omol25-dataset","title":"The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models","arxiv_id":"2505.08762","date":"2025-05-13","proceeding":null,"authors":["Daniel S. Levine","Muhammed Shuaibi","Evan Walter Clark Spotte-Smith","Michael G. Taylor","Muhammad R. Hasyim","Kyle Michel","Ilyes Batatia","Gábor Csányi","Misko Dzamba","Peter Eastman","Nathan C. Frey","Xiang Fu","Vahe Gharakhanyan","Aditi S. Krishnapriyan","Joshua A. Rackers","Sanjeev Raja","Ammar Rizvi","Andrew S. Rosen","Zachary Ulissi","Santiago Vargas","C. Lawrence Zitnick","Samuel M. Blau","Brandon M. Wood"],"abstract":"Machine learning (ML) models hold the promise of transforming atomic simulations by delivering quantum chemical accuracy at a fraction of the computational cost. Realization of this potential would enable high-throughout, high-accuracy molecular screening campaigns to explore vast regions of chemical space and facilitate ab initio simulations at sizes and time scales that were previously inaccessible. However, a fundamental challenge to creating ML models that perform well across molecular chemistry is the lack of comprehensive data for training. Despite substantial efforts in data generation, no large-scale molecular dataset exists that combines broad chemical diversity with a high level of accuracy. To address this gap, Meta FAIR introduces Open Molecules 2025 (OMol25), a large-scale dataset composed of more than 100 million density functional theory (DFT) calculations at the $\\omega$B97M-V/def2-TZVPD level of theory, representing billions of CPU core-hours of compute. OMol25 uniquely blends elemental, chemical, and structural diversity including: 83 elements, a wide-range of intra- and intermolecular interactions, explicit solvation, variable charge/spin, conformers, and reactive structures. There are ~83M unique molecular systems in OMol25 covering small molecules, biomolecules, metal complexes, and electrolytes, including structures obtained from existing datasets. OMol25 also greatly expands on the size of systems typically included in DFT datasets, with systems of up to 350 atoms. In addition to the public release of the data, we provide baseline models and a comprehensive set of model evaluations to encourage community engagement in developing the next-generation ML models for molecular chemistry.","url_abs":"https://arxiv.org/abs/2505.08762v1","url_pdf":"https://arxiv.org/pdf/2505.08762v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"the-open-molecules-2025-omol25-dataset","repo_url":"https://github.com/arif-phychem/datasets_and_databases_4_mlps","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"the-open-molecules-2025-omol25-dataset","repo_url":"https://github.com/prescient-design/StrainRelief","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2505.08762","atlas_url":"https://app.syntology.ai/?focus=2505.08762","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.08762"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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