{"url":"/dataset/jarvis-dft-formation-energy","name":"JARVIS-DFT","full_name":null,"description_markdown":"JARVIS-DFT is a repository of density functional theory based calculation data for materials.","description_withheld":null,"homepage":"https://jarvis.nist.gov/","introduced_date":"2020-07-03","introduced_date_note":null,"introduced_by":{"paper":"/paper/jarvis-an-integrated-infrastructure-for-data","title":"The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design","first_author":null,"url":null},"license":null,"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"}],"languages":[],"variants":["JARVIS-DFT"],"data_loaders":[],"num_papers_in_archive":21,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/formation-energy-on-jarvis-dft-formation","task":"Formation Energy","dataset_variant":"JARVIS-DFT","rows":6,"metrics":["MAE"],"first_row_in_archive_order":{"model":"CartNet","paper":"/paper/a-cartesian-encoding-graph-neural-network-for","metrics":{"MAE":"0.02705"},"code_links":[{"title":"imatge-upc/CartNet","url":"https://github.com/imatge-upc/CartNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-cartesian-encoding-graph-neural-network-for","title":"A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation","date":"2025-01-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/efficient-approximations-of-complete","title":"Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction","date":"2023-06-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/periodic-graph-transformers-for-crystal","title":"Periodic Graph Transformers for Crystal Material Property Prediction","date":"2022-09-23","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":4,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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},{"paper":"/paper/atomistic-line-graph-neural-network-for","title":"Atomistic Line Graph Neural Network for Improved Materials Property Predictions","date":null,"rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":9,"samples_ran":4,"samples_unverified":5,"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."}