{"url":"/dataset/oqmd-v1-2","name":"OQMD v1.2","full_name":"The Open Quantum Materials Database","description_markdown":"The OQMD is a database of DFT calculated thermodynamic and structural properties of one million materials, created in Chris Wolverton's group at Northwestern University.\r\n\r\nThe OQMD v1.2 dataset for CGNN is downloadable from [this link](https://doi.org/10.5281/zenodo.7118055), which contains 561,888 materials. Its format is described in [here](https://github.com/Tony-Y/cgnn#dataset-files). The original data is available at [the OQMD website](https://oqmd.org).","description_withheld":null,"homepage":"https://github.com/Tony-Y/oqmd-v1.2-dataset-for-cgnn","introduced_date":"2019-05-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/crystal-graph-neural-networks-for-data-mining","title":"Crystal Graph Neural Networks for Data Mining in Materials Science","first_author":"Takenori Yamamoto","url":null},"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"Formation Energy","url":"/task/formation-energy","datasets_with_task":"/datasets/task/formation-energy"},{"name":"Band Gap","url":"/task/band-gap","datasets_with_task":"/datasets/task/band-gap"},{"name":"Total Magnetization","url":"/task/total-magnetization","datasets_with_task":"/datasets/task/total-magnetization"},{"name":"Materials Screening","url":"/task/materials-screening","datasets_with_task":"/datasets/task/materials-screening"}],"languages":[],"variants":["OQMD v1.2"],"data_loaders":[{"repo":"https://github.com/Tony-Y/oqmd-v1.2-dataset-for-cgnn","url":"https://github.com/Tony-Y/oqmd-v1.2-dataset-for-cgnn","frameworks":[]}],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/formation-energy-on-oqmd-v12","task":"Formation Energy","dataset_variant":"OQMD v1.2","rows":4,"metrics":["MAE"],"first_row_in_archive_order":{"model":"CGNN Ensemble","paper":"/paper/crystal-graph-neural-networks-for-data-mining","metrics":{"MAE":"30.5"},"code_links":[{"title":"Tony-Y/cgnn","url":"https://github.com/Tony-Y/cgnn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/crystal-graph-neural-networks-for-data-mining","title":"Crystal Graph Neural Networks for Data Mining in Materials Science","date":"2019-05-27","rows_on_this_dataset":4,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}