{"url":"/dataset/materials-project","name":"Materials Project","full_name":null,"description_markdown":"The **Materials Project** is a collection of chemical compounds labelled with different attributes. The labelling is performed by different simulations, most of them at DFT level of theory.\r\n\r\nThe dataset links:\r\n\r\n* [MP 2018.6.1](https://github.com/materialsvirtuallab/megnet/tree/master/mvl_models/mp-2018.6.1) (69,239 materials)\r\n* [MP 2019.4.1](https://github.com/materialsvirtuallab/megnet/tree/master/mvl_models/mp-2019.4.1) (133,420 materials)","description_withheld":null,"homepage":"https://materialsproject.org/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":null,"title":"The Materials Project: A materials genome approach to accelerating materials innovation","first_author":null,"url":"http://link.aip.org/link/AMPADS/v1/i1/p011002/s1&Agg=doi"},"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"}],"languages":[],"variants":["Materials Project"],"data_loaders":[],"num_papers_in_archive":157,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/formation-energy-on-materials-project","task":"Formation Energy","dataset_variant":"Materials Project","rows":9,"metrics":["MAE"],"first_row_in_archive_order":{"model":"CartNet","paper":"/paper/a-cartesian-encoding-graph-neural-network-for","metrics":{"MAE":"17.47"},"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/mt-cgcnn-integrating-crystal-graph","title":"MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction","date":"2018-11-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/neural-message-passing-with-edge-updates-for","title":"Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials","date":"2018-06-08","rows_on_this_dataset":2,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":22,"samples_ran":4,"samples_unverified":18,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/schnet-a-deep-learning-architecture-for","title":"SchNet - a deep learning architecture for molecules and materials","date":null,"rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/graph-networks-as-a-universal-machine","title":"Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals","date":null,"rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":0,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/171010324","title":"Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties","date":null,"rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":48,"samples_ran":15,"samples_unverified":33,"pointer_only_for_licence":4,"papers_with_no_sample_that_ran":1,"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."}