{"url":"/dataset/mp20","name":"MP20","full_name":"Metastable crystal structures from Materials Project","description_markdown":"MP20 (Xie et al., 2022) contains 45,231 metastable crystal structures from the Materials Project (Jain et al., 2013), each with up to 20 atoms and spanning 89 different element types.","description_withheld":null,"homepage":"https://huggingface.co/datasets/chaitjo/MP20_ADiT","introduced_date":"2021-10-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/crystal-diffusion-variational-autoencoder-for-1","title":"Crystal Diffusion Variational Autoencoder for Periodic Material Generation","first_author":"Tian Xie","url":null},"license":{"name":"MIT","url":null},"modalities":[],"tasks":[{"name":"Unconditional Crystal Generation","url":"/task/unconditional-crystal-generation","datasets_with_task":"/datasets/task/unconditional-crystal-generation"}],"languages":[],"variants":["MP20"],"data_loaders":[],"num_papers_in_archive":28,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/unconditional-crystal-generation-on-mp20","task":"Unconditional Crystal Generation","dataset_variant":"MP20","rows":3,"metrics":["DFT Stable, Unique, Novel Rate","Validity"],"first_row_in_archive_order":{"model":"ADiT","paper":"/paper/all-atom-diffusion-transformers-unified","metrics":{"DFT Stable, Unique, Novel Rate":"6.0","Validity":"91.92"},"code_links":[{"title":"facebookresearch/all-atom-diffusion-transformer","url":"https://github.com/facebookresearch/all-atom-diffusion-transformer"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/all-atom-diffusion-transformers-unified","title":"All-atom Diffusion Transformers: Unified generative modelling of molecules and materials","date":"2025-03-05","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":6,"samples_unverified":1,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/flowllm-flow-matching-for-material-generation","title":"FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions","date":"2024-10-30","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/flowmm-generating-materials-with-riemannian","title":"FlowMM: Generating Materials with Riemannian Flow Matching","date":"2024-06-07","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"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":3,"samples_harvested":9,"samples_ran":8,"samples_unverified":1,"pointer_only_for_licence":9,"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."}