{"url":"/dataset/oqm9hk","name":"OQM9HK","full_name":null,"description_markdown":"This is a large-scale dataset of quantum-mechanically calculated properties (DFT level) of crystalline materials for graph representation learning that contains approximately 900k entries (OQM9HK). This dataset is constructed on the basis of [the Open Quantum Materials Database](https://oqmd.org) (OQMD) v1.5 containing more than one million entries, and is the successor to [the OQMD v1.2 dataset](https://paperswithcode.com/dataset/oqmd-v1-2) containing approximately 600k entries (OQM6HK).\r\n\r\n* [Technical Report](https://storage.googleapis.com/rimcs_cgnn/oqm9hk_dataset_Sep_30_2022.pdf)\r\n* [CGNN v1.1](https://github.com/Tony-Y/cgnn/tree/dev_v1.1)\r\n\r\n![Histograms of materials properties](https://www.researchgate.net/profile/Takenori-Yamamoto-2/publication/364167371/figure/fig1/AS:11431281088057369@1664971652840/Histograms-of-formation-energy-volume-deviation-band-gap-and-total-magnetization-in_W640.jpg)","description_withheld":null,"homepage":"https://doi.org/10.5281/zenodo.7124330","introduced_date":"2022-09-30","introduced_date_note":null,"introduced_by":null,"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[],"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"}],"languages":[],"variants":["OQM9HK"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/formation-energy-on-oqm9hk","task":"Formation Energy","dataset_variant":"OQM9HK","rows":4,"metrics":["MAE"],"first_row_in_archive_order":{"model":"CGNN Full Ensemble","paper":"/paper/oqm9hk-a-large-scale-graph-dataset-for","metrics":{"MAE":"0.03433"},"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/oqm9hk-a-large-scale-graph-dataset-for","title":"OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials Science","date":"2022-09-30","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/schnet-a-continuous-filter-convolutional","title":"SchNet: A continuous-filter convolutional neural network for modeling quantum interactions","date":"2017-06-26","rows_on_this_dataset":1,"code_links":5,"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."}