{"as_of":"2026-08-10T00:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7c6d9d795849e53ed6b27e811e35b92695f12593c561d0d09ead76599b4ab521","coverage":[{"denominator":131,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T13:47:39.938304Z","state":"measured"},{"denominator":101,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":101,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T21:42:05.252001Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.02016","snapshot_observed_at":"2026-08-03T21:42:05.252001Z","title":"Zhisheng Xiao, Karsten Kreis, and Arash Vahdat","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.14426","last_updated":"2026-05-28T17:48:10Z","snapshot_observed_at":"2026-08-09T19:49:22.596199Z","submitted_at":"2025-11-18T12:29:19Z","title":"MiAD: Mirage Atom Diffusion for De Novo Crystal Generation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T21:42:05.252001Z"},"links":{"cited_paper":"/paper/2502.02016","citing_paper":"/paper/2511.14426"},"observation_digest":"sha256:6f2e0e4702ff35844b4b87eeffdf32908be4852e00d4b3b78cb93b37de77c24f","observation_id":"7940d7f6-5732-4c60-898a-e64ad48de7ba","resolution":{"observed_at":"2026-08-03T21:42:05.252001Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.02016/citation-record","integrity":"/paper/2502.02016/integrity","json":"/paper/2502.02016/citation-record.json","paper":"/paper/2502.02016"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.650352Z","title":"Crystal-gfn: sampling crystals with desirable properties and constraints","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.650352Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:65b7bb1de68206667adc6028c2ff5d7fe36c3f6f3f4f83efe6e1906609346286","observation_id":"ee28b7e7-0704-496e-9dff-92cc07423f37","resolution":{"observed_at":"2026-08-09T13:47:39.650352Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.654842Z","title":"Equivariant energy-guided SDE for inverse molecular design","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.654842Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:467281c84068fe3e0888274ee8a339c41414b9aa6439d9a7174a16fa11f974aa","observation_id":"077133bf-bfb1-4f4c-80bb-9e9b4d3ae2ba","resolution":{"observed_at":"2026-08-09T13:47:39.654842Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.658060Z","title":"Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.658060Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:bfeef483a3378fa7768fe570ce3127d9b32311dc313c98ac71cd518f9fd80a38","observation_id":"52f1926b-9426-4449-bcf0-381e3eaa0bff","resolution":{"observed_at":"2026-08-09T13:47:39.658060Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.660876Z","title":"Projector augmented-wave method","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.660876Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:452ccca166f34a420217ac8f2fac9d16935492c9c216f8ef07e4ab904cfe4d42","observation_id":"54bc5098-363a-47c9-ac96-622f313a2a6f","resolution":{"observed_at":"2026-08-09T13:47:39.660876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.663840Z","title":"Riemannian score-based generative modelling","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.663840Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:0a5b2c6bedf0543248599a009b765c9d0522742e7f790abf4180799afb00c474","observation_id":"85ce46c2-74f2-48f8-ab65-b6463f30a175","resolution":{"observed_at":"2026-08-09T13:47:39.663840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.666857Z","title":"Geometry optimization of periodic systems using internal coordinates","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.666857Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:25523f4c911b224d329b3fdf075c2cc1b4b0c4030b948dd7b34a9732e81bca13","observation_id":"59f821cd-f8e9-4245-ae0a-af1431ce4216","resolution":{"observed_at":"2026-08-09T13:47:39.666857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.670157Z","title":"Machine learning for molecular and materials science","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.670157Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:13fe1af0e7e17efcb2556f178bb05d7ba593ecf29621a3a43557d68705c95c54","observation_id":"da948cbd-7039-4622-b094-5b7fd28f7588","resolution":{"observed_at":"2026-08-09T13:47:39.670157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.673403Z","title":"Space group informed transformer for crystalline materials generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.673403Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:5db7d5a8be3ecb8a1876570e00a0b073b477b2a31535901ad5319872df3b8047","observation_id":"4d0d6a45-dc22-4952-bd79-6017bb7e7410","resolution":{"observed_at":"2026-08-09T13:47:39.673403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.676342Z","title":"New cubic perovskites for one-and two-photon water splitting using the computational materials repository","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.676342Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:c026f84e94145e2049ff65ff65a03a0fc5121995d16014c24850555260e508c0","observation_id":"9594f7b5-17de-4859-a7b2-1781a68bec43","resolution":{"observed_at":"2026-08-09T13:47:39.676342Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.679122Z","title":"Computational screening of perovskite metal oxides for optimal solar light capture","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.679122Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:c820126c2c313ccb8d31003016e44e3b4c718e8e19b80e895327b3f44fc2d36c","observation_id":"edfdcb27-2a7d-45da-92b2-8a30f0f95c58","resolution":{"observed_at":"2026-08-09T13:47:39.679122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.682121Z","title":"Lawrence Zitnick, and Zachary Ulissi","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.682121Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:43e3bb1e6753b079c0c404fc8cb2be1de3aa19b27134bf980d01db1147ccb6d1","observation_id":"5e82b11a-10d7-4ba9-9c37-ad8df0532844","resolution":{"observed_at":"2026-08-09T13:47:39.682121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.685144Z","title":"A universal graph deep learning interatomic potential for the periodic table","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.685144Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:1b9d62ac0580939d00580c719c8658d54e3425cba9ed6f6ee6b26f828d74e0f7","observation_id":"188af516-4647-45b7-945c-2beb0ff9e7c6","resolution":{"observed_at":"2026-08-09T13:47:39.685144Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.688112Z","title":"A universal graph deep learning interatomic potential for the periodic table","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.688112Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:15dde4f505d316d95b83efa0b1ab13c36deac1c05c132d647563e7852c4ad71c","observation_id":"b308ac0d-4b5f-4dac-afdd-26a93a36a17a","resolution":{"observed_at":"2026-08-09T13:47:39.688112Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.691047Z","title":"Graph networks as a universal machine learning framework for molecules and crystals","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.691047Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:c88558722b9036e9fd14c2528b41a736fe9be138d24558162e774fbb78b00b47","observation_id":"a8a0023b-5c59-4b40-9feb-a4358687e92d","resolution":{"observed_at":"2026-08-09T13:47:39.691047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.03660","last_updated":"2024-02-26T17:52:00Z","snapshot_observed_at":"2026-08-07T07:19:37.949231Z","submitted_at":"2023-02-07T18:21:24Z","title":"Flow Matching on General Geometries","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.03660","snapshot_observed_at":"2026-08-09T13:47:39.693805Z","title":"Riemannian flow matching on general geometries","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.693805Z"},"links":{"cited_paper":"/paper/2302.03660","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:c63bd690792ed3cb2507bf882b6100ee4fef944149a50d0d71e0f01b6ec87cde","observation_id":"8d04fa71-5a32-4483-857a-670330146612","resolution":{"observed_at":"2026-08-09T13:47:39.693805Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.696778Z","title":"Crystal structure prediction by combining graph network and optimization algorithm","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.696778Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:d183e56e73cd23e3f6c696f4c6fb13610752fb1b6a6c358150afa91311d64a4a","observation_id":"f85a749e-0442-46bd-b9d7-d5523f88c194","resolution":{"observed_at":"2026-08-09T13:47:39.696778Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.699566Z","title":"Jaakkola","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.699566Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:e663f1e126ea4a817fbec55f103122a94ed830b64dc524a480182f7f058d3347","observation_id":"93e10b46-e790-4c3f-901e-76aee6b4b34c","resolution":{"observed_at":"2026-08-09T13:47:39.699566Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.702270Z","title":"3-d inorganic crystal structure generation and property prediction via representation learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.702270Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:f3149154ea22b8579d3e77e16a52ce2f2cf3d72d609ba20d14150198de949e5b","observation_id":"70466178-8034-42c7-920b-dc79af8af0f0","resolution":{"observed_at":"2026-08-09T13:47:39.702270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.704913Z","title":"Smact: Semiconducting materials by analogy and chemical theory","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.704913Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:68470bbb12e66fd410ae95cc7e7fe3706fc578637d32c008d618bc544277c795","observation_id":"a9da4f39-1d8e-41d4-8ec6-28f2febeaca5","resolution":{"observed_at":"2026-08-09T13:47:39.704913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.707709Z","title":"Cryptic crystallography","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.707709Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:79b4cfe12b5583028ec79b4b54f134286b151962f08ff1e9f3958738f04313a7","observation_id":"f694a011-6401-470c-8517-e4449cef5480","resolution":{"observed_at":"2026-08-09T13:47:39.707709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.710431Z","title":"Chiral and achiral crystal structures","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.710431Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:fb9939b1f428cb6600c610a291c1bc3ba6f7cfc7b6dc6961ec67b390006dd0c3","observation_id":"cf0c6af3-61eb-4f09-8f2d-4f990971324e","resolution":{"observed_at":"2026-08-09T13:47:39.710431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:40.555392Z","title":"Pyxtal: A python library for crystal structure generation and symmetry analysis","venue":null,"work_id":"17c657ee-abf9-4343-a2a7-d1ee9d6304e1","year":2021},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.714402Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:c749008d60c2953821176fd24053eac8c5c95fd5d180523bd2a40add7e73f751","observation_id":"da2b5050-2a2c-4377-9ce1-af1fe6a84636","resolution":{"observed_at":"2026-08-09T13:47:40.558399Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.717070Z","title":"Worrall, Volker Fischer, and Max Welling","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.717070Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:46c24078d713b704f4dd330f3ab0300b71bf8ace41cb895758d0bf502889bfa6","observation_id":"1b0feab5-30bf-44b5-9dcf-3e76e86e037a","resolution":{"observed_at":"2026-08-09T13:47:39.717070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.719761Z","title":"Margraf, and Stephan G \\\"u nnemann","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.719761Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:1ce3866ab454df74946c96ab9557dd84285c37929d2eb3c6c0b2f43f49102d6b","observation_id":"1f2f6c4d-d7da-4d39-9484-32ebc523cc9f","resolution":{"observed_at":"2026-08-09T13:47:39.719761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.723199Z","title":"Gemnet: Universal directional graph neural networks for molecules","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.723199Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:491359857a6bc83ba74ea2357cfbf0d0c48e3ac6a0a7719f97cb5c008aaa30d9","observation_id":"58a96c16-4a1d-432b-bdeb-c8bfdfc76b14","resolution":{"observed_at":"2026-08-09T13:47:39.723199Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.726121Z","title":"Gemnet: Universal directional graph neural networks for molecules","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.726121Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:c98ccbde08c7a0fd32caed8ac3cca327c8626f707190ad66691d798aec5d4d5e","observation_id":"0978d8ca-76a8-4f59-a41e-f35895774b87","resolution":{"observed_at":"2026-08-09T13:47:39.726121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.728739Z","title":"Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.728739Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:ebed16166be23a44477130f3d1607f4a548456f2908c650c64c0451798aa4397","observation_id":"df1c0a7a-bb38-4b47-9528-9062c2e1451e","resolution":{"observed_at":"2026-08-09T13:47:39.728739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.731438Z","title":"u ller, and Kristof T Sch \\","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.731438Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:187febfc1735a17380d670543303e309a454a95e02b7871ca310776d84d9dea1","observation_id":"f37cb14c-0129-4610-a32b-47fbbc16706e","resolution":{"observed_at":"2026-08-09T13:47:39.731438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.734287Z","title":"Neural message passing for quantum chemistry","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.734287Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:ef5493fdfcf7d1addfa9698b286fa4d557668af5b3bbf665d62a1a325493e6c4","observation_id":"81e447bd-f748-4542-b3e2-5068f3b9c0da","resolution":{"observed_at":"2026-08-09T13:47:39.734287Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.737122Z","title":"Uspex—evolutionary crystal structure prediction","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.737122Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:63beb8a8cb8fd71e434fb90d77d3c966a7cdc9d27d5db285ef6410a2ed9c8f53","observation_id":"d81bb0e0-f159-4f0d-bf04-b7a1b61c4249","resolution":{"observed_at":"2026-08-09T13:47:39.737122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.739804Z","title":"Generative adversarial networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.739804Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:9a381befcaee3762a7e7adef43b919dd0a5035417028cb650af6588478251e81","observation_id":"a773a09b-b21e-4943-b1c8-473b3e0ce9cb","resolution":{"observed_at":"2026-08-09T13:47:39.739804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.07037","last_updated":"2025-03-11T08:07:39Z","snapshot_observed_at":"2026-07-06T16:05:55.074136Z","submitted_at":"2023-08-14T09:56:35Z","title":"Bayesian Flow Networks","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.07037","snapshot_observed_at":"2026-08-09T13:47:39.743088Z","title":"Bayesian flow networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.743088Z"},"links":{"cited_paper":"/paper/2308.07037","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:fab982f4b9bb9c064cb3a24d37f231172b297d0532663252dfaaa7e668846b73","observation_id":"04930127-db61-4db1-9ac8-06bfb812f210","resolution":{"observed_at":"2026-08-09T13:47:39.743088Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.746301Z","title":"Numerically stable algorithms for the computation of reduced unit cells","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.746301Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:1aecbad04fb43b281346f53525faf6e8ab79f1f63c3af5cceb3aa1c7b207d718","observation_id":"09f35291-1727-4fc0-abf8-bd69a9dede27","resolution":{"observed_at":"2026-08-09T13:47:39.746301Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04379","last_updated":"2025-07-24T04:36:04Z","snapshot_observed_at":"2026-08-09T14:33:01.731400Z","submitted_at":"2024-02-06T20:35:28Z","title":"Fine-Tuned Language Models Generate Stable Inorganic Materials as Text","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04379","snapshot_observed_at":"2026-08-09T13:47:39.749121Z","title":"Fine-tuned language models generate stable inorganic materials as text","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.749121Z"},"links":{"cited_paper":"/paper/2402.04379","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:65d1b79e555944c836d3ab465eb2d484d7b0ace7fdc13d8aaa2853db1b8b7552","observation_id":"73508491-4329-4be5-8593-38552eb0474d","resolution":{"observed_at":"2026-08-09T13:47:39.749121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.752193Z","title":"Finding the location of a signal: A bayesian analysis","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.752193Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:2a42e8bd2d44d9779502d275a231b18077f87343c5e7024512948700ae298666","observation_id":"f09804e8-505a-4cb4-aab8-ebf4f09d414b","resolution":{"observed_at":"2026-08-09T13:47:39.752193Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.755039Z","title":"Ab-initio simulations of materials using vasp: Density-functional theory and beyond","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.755039Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:1cb7666fb3a8661a9454dd0bec0fd77370cdd93b317d68b23922bded7003980c","observation_id":"bfe5ab3b-92aa-4f21-a316-b5c230c06b55","resolution":{"observed_at":"2026-08-09T13:47:39.755039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.757780Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.757780Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:02e1212977cfdce437325ed7708c20d823e146bbd1c966ecbe1cc3c1cf0967a7","observation_id":"2da7ef27-d067-4858-9c90-58424270fc8e","resolution":{"observed_at":"2026-08-09T13:47:39.757780Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.760457Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.760457Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:2d76c58a55ac9caffda85ce839a489596722e7d143440f4f49e917d163d72903","observation_id":"743e9deb-41fc-4e8a-a66b-28344d7eb6a9","resolution":{"observed_at":"2026-08-09T13:47:39.760457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.00949","last_updated":"2019-09-03T04:36:13Z","snapshot_observed_at":"2026-08-04T15:03:41.789416Z","submitted_at":"2019-09-03T04:36:13Z","title":"Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.00949","snapshot_observed_at":"2026-08-09T13:47:39.763315Z","title":"Data-driven approach to encoding and decoding 3-d crystal structures","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.763315Z"},"links":{"cited_paper":"/paper/1909.00949","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:c7fcf971b1d3ca886bf9c6f5bc7afd4c30aa52eba0d99db733d9bbb3231786c7","observation_id":"a0a91dae-43fb-495c-a0e0-3f1507d8c556","resolution":{"observed_at":"2026-08-09T13:47:39.763315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.766464Z","title":"Crystal structure prediction by data mining","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.766464Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:962be044ab7ff122c62ced95decf0917eb73a8e89e436051d14ee4e177f8de74","observation_id":"1a2f0014-1516-4d77-8d32-94512414332b","resolution":{"observed_at":"2026-08-09T13:47:39.766464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.769258Z","title":"Argmax flows and multinomial diffusion: Learning categorical distributions","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.769258Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:4c75c216cdff1461f7edb6c3c2846deb0524acd9084dee4125b7087274ba4bfb","observation_id":"57c972fc-7a0e-4c8d-b737-4c787bf44422","resolution":{"observed_at":"2026-08-09T13:47:39.769258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.772234Z","title":"Equivariant diffusion for molecule generation in 3d","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.772234Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:a9a4319964bc653008ab856550e4280fb8b3d7e128bd5a7efe0b5b638b0613b4","observation_id":"41d312b0-3d7f-40e4-aeb8-ea9b36f0d5e1","resolution":{"observed_at":"2026-08-09T13:47:39.772234Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.774842Z","title":"Distance matrix-based crystal structure prediction using evolutionary algorithms","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.774842Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:1ad8d0140acd788509ac9a892cdd1048254cdbaff5e6843ecc195c85017d8766","observation_id":"e2d23059-3f7f-4555-a890-1659403da261","resolution":{"observed_at":"2026-08-09T13:47:39.774842Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.777485Z","title":"Contact map based crystal structure prediction using global optimization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.777485Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:e8eba2b344137cdaf5c60a68e7c02f00c7d1dc11763d5fc75d48a14471f9a6fc","observation_id":"1b949797-f71b-403e-91b8-0ef5bcf21696","resolution":{"observed_at":"2026-08-09T13:47:39.777485Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.780134Z","title":"Riemannian diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.780134Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:7151016759f6e236daf69c1c49b3c469e3f661a12dfb82739d23c8ddd5e6ad31","observation_id":"cebbcebb-8035-41a6-9e8c-197bbab07c28","resolution":{"observed_at":"2026-08-09T13:47:39.780134Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.782832Z","title":"On-the-fly machine learning of atomic potential in density functional theory structure optimization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.782832Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:21e46b57cf8bb0c3837f0e3db8bc0149f0fcf1387109c98ee2342eb63c87f85a","observation_id":"e557c1e6-151b-4fa7-8222-73dd807de144","resolution":{"observed_at":"2026-08-09T13:47:39.782832Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.785651Z","title":"Commentary: The materials project: A materials genome approach to accelerating materials innovation","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.785651Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:9b71cfeea9a05eb2a5e8b5738e30f881049940ee34436a817d840689aa1bd27d","observation_id":"498f2ff1-d2a3-45ae-8c0b-309f024314db","resolution":{"observed_at":"2026-08-09T13:47:39.785651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.788368Z","title":"Crystal structure prediction by joint equivariant diffusion on lattices and fractional coordinates","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.788368Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:17b0ad899bb53930db58f079d679314bcd79784c4743a480fca8716e4cae1343","observation_id":"27fb15b9-9624-404d-8b27-1478f4598efd","resolution":{"observed_at":"2026-08-09T13:47:39.788368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03992","last_updated":"2024-04-08T04:44:23Z","snapshot_observed_at":"2026-08-09T05:29:56.541225Z","submitted_at":"2024-02-06T13:45:01Z","title":"Space Group Constrained Crystal Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03992","snapshot_observed_at":"2026-08-09T13:47:39.791100Z","title":"Space group constrained crystal generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.791100Z"},"links":{"cited_paper":"/paper/2402.03992","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:2e0cb3d4e57a8a6f0a26db734cb65058b29c0daf17a9c9480f4c82a6dfc29a2f","observation_id":"869f5366-922f-4438-96ce-0fdb11f5b5e7","resolution":{"observed_at":"2026-08-09T13:47:39.791100Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.794092Z","title":"Torsional diffusion for molecular conformer generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.794092Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:fbc1e0a0658d61f2c1c8c1b72a1d54e4aa911b586c0fa8888227fd895cb6d486","observation_id":"237dd33c-9c45-42b7-9367-4e281b26b0fb","resolution":{"observed_at":"2026-08-09T13:47:39.794092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.796787Z","title":"Highly accurate protein structure prediction with AlphaFold","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.796787Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:d049c4ee77ce1eabbd3a75177099ee94af72f3037bbb06b0b1f0e12e8e51fcb3","observation_id":"e4e0d3bf-d3bf-4eab-b22d-6e629f114a22","resolution":{"observed_at":"2026-08-09T13:47:39.796787Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.799488Z","title":"Generative adversarial networks for crystal structure prediction","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.799488Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:9e3215e174fc4a8bab760b7cc5c9c0ba9704d55189777808825dee7435a0a4ac","observation_id":"c5b0cf66-f5fc-4196-bfa7-cadda23c6966","resolution":{"observed_at":"2026-08-09T13:47:39.799488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.07308","last_updated":"2016-11-21T11:37:17Z","snapshot_observed_at":"2026-08-06T06:17:37.090691Z","submitted_at":"2016-11-21T11:37:17Z","title":"Variational Graph Auto-Encoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.07308","snapshot_observed_at":"2026-08-09T13:47:39.802113Z","title":"Variational graph auto-encoders","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.802113Z"},"links":{"cited_paper":"/paper/1611.07308","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:22d0788ad7d2d25f12059d7efd130618d289bdb9f31d63358b483b9733398aff","observation_id":"9b6a5313-6e12-40c9-8142-daad25205fab","resolution":{"observed_at":"2026-08-09T13:47:39.802113Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.05192","last_updated":"2022-11-19T13:35:12Z","snapshot_observed_at":"2026-08-09T18:34:16.478555Z","submitted_at":"2022-02-10T17:50:21Z","title":"von Mises-Fisher distributions and their statistical divergence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.05192","snapshot_observed_at":"2026-08-09T13:47:39.805205Z","title":"von mises-fisher distributions and their statistical divergence","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.805205Z"},"links":{"cited_paper":"/paper/2202.05192","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:e78b4aeee4b716f98e4032827a9b53161563e536c85b9e8392b3bfeb0e0ca017","observation_id":"b525643b-0382-4ee5-b277-6939a2b12856","resolution":{"observed_at":"2026-08-09T13:47:39.805205Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.04510","last_updated":"2023-06-07T15:23:59Z","snapshot_observed_at":"2026-08-09T14:33:25.883783Z","submitted_at":"2023-06-07T15:23:59Z","title":"Unified Model for Crystalline Material Generation","version":1},"cited_work":{"arxiv_id":"2306.04510","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.04510","snapshot_observed_at":"2026-08-09T13:47:40.362584Z","title":"Unified Model for Crystalline Material Generation","venue":"cond-mat.mtrl-sci","work_id":"a5ba2722-0c15-4049-a91a-bbc4c5cb998b","year":2023},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.808067Z"},"links":{"cited_paper":"/paper/2306.04510","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:0d1eb2d264407260e4fc07607f2c92d112c9b87d4b527e12f538e9bf70d8c752","observation_id":"ee69c14c-7f60-4075-9c99-883be72d37a0","resolution":{"observed_at":"2026-08-09T13:47:40.365785Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.810933Z","title":"Self-consistent equations including exchange and correlation effects","venue":null,"work_id":null,"year":1965},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.810933Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:05e20ce149a26282ed35d4b614d9b5dcf52d68024c90fb46e2c5a05412fe4af9","observation_id":"9adcd8f6-e50b-4ef4-a220-d4b4e7b42874","resolution":{"observed_at":"2026-08-09T13:47:39.810933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.813898Z","title":"u rgen Furthm \\","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.813898Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:afac2b498fd14f2be5bbeec78bfd71e8282cd7bbcd072ad8c7b546aa27c94f8f","observation_id":"d9d4a95b-cf25-44f5-b3cd-2563bb2df506","resolution":{"observed_at":"2026-08-09T13:47:39.813898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.816634Z","title":"Efficient evaluation of the probability density function of a wrapped normal distribution","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.816634Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:26b3b54aad83ab15caed175ee11efc42454bf9a2feaa7bdf66b3c5501fbbb882","observation_id":"924df85f-6d9b-4fe1-a69b-84b1d61c9e35","resolution":{"observed_at":"2026-08-09T13:47:39.816634Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.819463Z","title":"Crystal structure prediction with machine learning-based element substitution","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.819463Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:5348b55c2874beeed1f2f4ebb9d03b4bbdb3f29dd4c342d5582db38e10817428","observation_id":"006788e9-51cd-4eb2-8894-6e14d050e2d5","resolution":{"observed_at":"2026-08-09T13:47:39.819463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.822094Z","title":"Modern directional statistics","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.822094Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:abc85d6f2359b14827f505425f8e94d185625b654d69a6117a81e8642b35712e","observation_id":"f2e6803a-f4ae-419e-b9da-7fc67050b654","resolution":{"observed_at":"2026-08-09T13:47:39.822094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.824924Z","title":"Equivariant diffusion for crystal structure prediction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.824924Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:ea3e7b14235eb940e62b98288ad65be8aa43561e53a5beced9410689f8b4456f","observation_id":"de2fa8fa-cf4a-4f93-9830-d5f0aeb8e37a","resolution":{"observed_at":"2026-08-09T13:47:39.824924Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02747","last_updated":"2023-02-08T15:46:05Z","snapshot_observed_at":"2026-08-02T18:24:58.914589Z","submitted_at":"2022-10-06T08:32:20Z","title":"Flow Matching for Generative Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02747","snapshot_observed_at":"2026-08-09T13:47:39.827746Z","title":"Flow matching for generative modeling","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.827746Z"},"links":{"cited_paper":"/paper/2210.02747","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:6dbab60653c09b1ac1699e1ec727c2217bd7e54c3a848eaf3deadb5284d38678","observation_id":"2cc178d1-be68-45bc-8b9b-d360e0aec010","resolution":{"observed_at":"2026-08-09T13:47:39.827746Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.jmat.2017.08.002","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:40.075004Z","title":"Materials discovery and design using machine learning","venue":null,"work_id":"71119366-f720-493e-93b4-c0b49e79cd58","year":2017},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.830635Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:6f68d606c76cbf043d09e7e506341156c0069557134327e5557e0b603a86dbc6","observation_id":"226d6a51-f876-4fd8-8c03-dd563dac9449","resolution":{"observed_at":"2026-08-09T13:47:40.079586Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.833468Z","title":"Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.833468Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:a01ba711e14ea17c4f06ec66eac2ca9589492e40c72e25ce3b2768b495e772ba","observation_id":"e0e7a71d-c204-45f6-88fc-36d7f9faf817","resolution":{"observed_at":"2026-08-09T13:47:39.833468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.836405Z","title":"Deep learning generative model for crystal structure prediction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.836405Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:cff2a58ee4b4416bba14c129798f7a87bd65324762ab855e54222a30982bdb97","observation_id":"d15ab7af-16fa-48f9-902d-dffd70400d8d","resolution":{"observed_at":"2026-08-09T13:47:39.836405Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.213739Z","title":"Towards symmetry-aware generation of periodic materials","venue":null,"work_id":"a4fd6767-a674-4595-9ca6-88bfd0730f5d","year":2024},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.839126Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:b238fb82e7fd3815c2654dfc977ffa3f0a50baed8d09fbeded977c5acf6119a6","observation_id":"b50febc7-9e6a-40d6-b149-17376ad5263c","resolution":{"observed_at":"2026-08-09T13:47:41.216878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.205358Z","title":"Bayesian inference for the von mises-fisher distribution","venue":null,"work_id":"4e1e1928-907b-41cb-b8d8-353b5721eb7b","year":1976},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.842603Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:b9e7e76a052aa491d1f6080533d3b8bcaacdb9d0d7ede965f022fff36a9a800d","observation_id":"2b283427-1f04-4ae3-a72a-76af503d7e1d","resolution":{"observed_at":"2026-08-09T13:47:41.208269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.845392Z","title":"Directional statistics","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.845392Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:80a3b5c97b06634b96753f079f373caf8f34104e74d42e7a303d1ddf247e2121","observation_id":"c6db39f9-bf56-4610-b204-e4823b9d59f3","resolution":{"observed_at":"2026-08-09T13:47:39.845392Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.192469Z","title":null,"venue":null,"work_id":"c4190008-0c07-4569-b957-16bb49b1573a","year":2024},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.848073Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:bc32abe512fea95599349609e7a62d7ab4f5676d8a16eb4bb577dfec1d40b04b","observation_id":"61b6cff5-79db-467b-b1b8-8ac65d2c7136","resolution":{"observed_at":"2026-08-09T13:47:41.195717Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.851044Z","title":"Improved denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.851044Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:2613468309ee051933172fe4ed5d4022048b5ba87f12b037ec923c5821236dab","observation_id":"e4272419-55dc-4f9a-82c2-fb9cdc3cc7fb","resolution":{"observed_at":"2026-08-09T13:47:39.851044Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.179697Z","title":"Krystallographische und strukturtheoretische Grundbegriffe, volume 1","venue":null,"work_id":"781df675-1bd5-4d3c-9e63-89b0c3468531","year":1928},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.853714Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:933f5e39e893d5fafc561b893fb165f024d2556ef6c43ad3806ec1ad0952ef68","observation_id":"d14153c4-122a-4f4e-bb6c-9a38ec8247ba","resolution":{"observed_at":"2026-08-09T13:47:41.182756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.857460Z","title":"Inverse design of solid-state materials via a continuous representation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.857460Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:e71ed89d30f38d1cd6d3e9e964ff64c1ec647fb9624ca5cf9bcd832014ca1623","observation_id":"ea9700d4-79dd-49c7-b546-4e46885a9410","resolution":{"observed_at":"2026-08-09T13:47:39.857460Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.11203","last_updated":"2019-05-25T10:30:49Z","snapshot_observed_at":"2026-08-05T08:12:15.748442Z","submitted_at":"2018-10-26T06:50:04Z","title":"CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.11203","snapshot_observed_at":"2026-08-09T13:47:39.860377Z","title":"Crystalgan: learning to discover crystallographic structures with generative adversarial networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.860377Z"},"links":{"cited_paper":"/paper/1810.11203","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:d674cfb038527353601639409f8da98377d1b6b26f8f988c7e4ecb66e7a27777","observation_id":"ed676526-dae4-4522-a3e6-56ef3909cd4b","resolution":{"observed_at":"2026-08-09T13:47:39.860377Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.166129Z","title":"Structure prediction drives materials discovery","venue":null,"work_id":"cd284665-4abe-4d83-88a1-e64dcd2530e5","year":2019},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.863520Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:4ae3e0445479ee289650b87207d2700f3c0d377e9a32bc4b357ec58fde2bc4eb","observation_id":"54f3b064-6131-4771-9d23-c0f66edd7b90","resolution":{"observed_at":"2026-08-09T13:47:41.169089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.157716Z","title":"Python materials genomics (pymatgen): A robust, open-source python library for materials analysis","venue":null,"work_id":"fa340782-d1d6-4acd-ab8b-d1bfd4814354","year":2013},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.866319Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:7653cce7bfda3dc2c77cc57641d53a33f4ba29d0c2bcaa7cdaa0ce60287e0afd","observation_id":"3d6b29ce-4463-41da-85f5-8a1f4d246ffc","resolution":{"observed_at":"2026-08-09T13:47:41.160765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.149252Z","title":"Human-and machine-centred designs of molecules and materials for sustainability and decarbonization","venue":null,"work_id":"8d32c9b1-4f0f-4488-bceb-3276ca116f23","year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.868932Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:11c3df0195bef88dc4ac55cbc354938cea9ad9ba8d9838adbca3fe17671ab7fb","observation_id":"70fda61c-52c9-4b76-b06e-907a7285f42c","resolution":{"observed_at":"2026-08-09T13:47:41.152246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.140991Z","title":"Generalized gradient approximation made simple","venue":null,"work_id":"53d863f3-9f4b-41cc-8d48-e31c6da63a34","year":1996},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.872562Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:53676df3b586c64acbb98717c641e93fc51802c596982e223715e5bca993b670","observation_id":"879aa7e3-dbc7-4c28-b415-911969141a8b","resolution":{"observed_at":"2026-08-09T13:47:41.143982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.132702Z","title":null,"venue":null,"work_id":"5c1a1897-35d7-493e-a407-1e8d1ae82eb3","year":2020},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.875454Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:df3a62fa4327a0f2d5038ee1c3ed11d7d512b92fdbf15480406385fccb3275dc","observation_id":"3a074d5d-fe1a-4f93-8065-11afb0b89918","resolution":{"observed_at":"2026-08-09T13:47:41.135678Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.878141Z","title":"Ab initio random structure searching","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.878141Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:ac726ab59a99625fbdb66eb54f15bb3818148e4f344aaff6c3e972d0f745d4ae","observation_id":"fbe54389-b0d9-4ff6-8d7d-752365e54a8f","resolution":{"observed_at":"2026-08-09T13:47:39.878141Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.119911Z","title":"Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning","venue":null,"work_id":"dda452e7-1aeb-4d2e-9b44-ed1738979562","year":2019},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.880918Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:68045a191f6e20506706a95e3bd4b32b5c5d797d9af8bdd1646ee9cbecc12537","observation_id":"f6a56942-7512-4c8d-bf67-a601d0e348c5","resolution":{"observed_at":"2026-08-09T13:47:41.122927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.12141","last_updated":"2024-05-28T03:48:38Z","snapshot_observed_at":"2026-08-09T14:33:05.651089Z","submitted_at":"2024-04-18T12:43:39Z","title":"MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.12141","snapshot_observed_at":"2026-08-09T13:47:39.883697Z","title":"Molcraft: Structure-based drug design in continuous parameter space","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.883697Z"},"links":{"cited_paper":"/paper/2404.12141","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:d2d329dfe2e37fd82939944709ae58da11149dffe246febacb7a9d510c8ad41b","observation_id":"80de4b4a-7ba9-4335-bba2-047cc4ad671c","resolution":{"observed_at":"2026-08-09T13:47:39.883697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.886886Z","title":"Language models are unsupervised multitask learners","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.886886Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:e7e241ba746e6785d8c4cdaaa2d4c019bedcbe7abc6caa654faff86325b439a4","observation_id":"403d1f1f-714f-406f-9862-ddda5a6ce166","resolution":{"observed_at":"2026-08-09T13:47:39.886886Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06125","last_updated":"2022-04-13T01:10:33Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-13T01:10:33Z","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06125","snapshot_observed_at":"2026-08-09T13:47:39.889615Z","title":"Hierarchical text-conditional image generation with clip latents","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.889615Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:2cbca83b98bfa0dc572b352341af97610c9ef6f33de82153d031a093a504ae56","observation_id":"a804fe46-6719-428c-a4c9-2d97763a4c64","resolution":{"observed_at":"2026-08-09T13:47:39.889615Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.892545Z","title":"Aberle, Shijing Sun, Xiaonan Wang, Yi Liu, Qianxiao Li, Senthilnath Jayavelu, Kedar Hippalgaonkar, Yousung Jung, and Tonio Buonassisi","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.892545Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:b86b21dc75a33eb6a031760502225f9f1c2514737c5ac9a0bfd9fd53012c8df4","observation_id":"d551f0c7-9466-460b-934f-ba523a6eab43","resolution":{"observed_at":"2026-08-09T13:47:39.892545Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.895539Z","title":"Fokker-planck equation","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.895539Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:fc47b3b11e1e96e6de48ad841aad10838ff96d46ce800068e201644307a88d4e","observation_id":"58ec37d9-c710-4253-a302-c20e5f2dbc97","resolution":{"observed_at":"2026-08-09T13:47:39.895539Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.898143Z","title":"High-resolution image synthesis with latent diffusion models, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.898143Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:622b14d84f0dc1ce3cceb41fe70699391d8b1deabd8f9275fb6779172559e3d9","observation_id":"7f30b288-94aa-410f-8217-105500e511ed","resolution":{"observed_at":"2026-08-09T13:47:39.898143Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.097449Z","title":"E (n) equivariant graph neural networks","venue":null,"work_id":"3fe13c46-3652-4422-a8d7-17a11d34a9a3","year":2021},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.900866Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:00b8f95d0b9ca47c748c724472f6ed6b436fb26745684f6ce3007217f7a6a6f6","observation_id":"57899bcb-a995-40c7-b646-4f6ad36fd5e3","resolution":{"observed_at":"2026-08-09T13:47:41.100507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.089114Z","title":"Recent advances and applications of machine learning in solid-state materials science","venue":null,"work_id":"b3060871-7d73-488f-93df-30ea9368cda7","year":2019},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.903601Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:9c9ea97c503d2f36b3faa530089dff64b2996e7f8f11648a6550167a8bc02e45","observation_id":"03658b97-af4b-4ac7-aca8-1d8afbbf9b02","resolution":{"observed_at":"2026-08-09T13:47:41.092208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.080302Z","title":"u tt, Huziel E Sauceda, P-J Kindermans, Alexandre Tkatchenko, and K-R M \\","venue":null,"work_id":"f7c8149f-bf7a-4c5c-857a-f73ff3c78e49","year":2018},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.906264Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:85bf72cf8049a18f1d77a39e13de41d299dd1e5039775105310b915032aef52d","observation_id":"17ce2ba7-4bd3-48a4-8435-9b4a25ff177e","resolution":{"observed_at":"2026-08-09T13:47:41.083688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.071852Z","title":"Learning gradient fields for molecular conformation generation","venue":null,"work_id":"0ccb02eb-03e6-4dd0-bbb0-f0c0c72c6026","year":2021},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.909047Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:e1f9dd25ec53d6bc63bbdd86d077b1c7838333966633053304a26144145dff4c","observation_id":"582ba599-929d-415d-9060-b3ead9e4a11b","resolution":{"observed_at":"2026-08-09T13:47:41.074928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.08761","last_updated":"2023-03-02T07:09:46Z","snapshot_observed_at":"2026-08-09T14:34:26.323287Z","submitted_at":"2022-10-17T06:00:12Z","title":"Protein Sequence and Structure Co-Design with Equivariant Translation","version":2},"cited_work":{"arxiv_id":"2210.08761","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.08761","snapshot_observed_at":"2026-08-09T13:47:40.321880Z","title":"Protein Sequence and Structure Co-Design with Equivariant Translation","venue":"q-bio.BM","work_id":"85bc047c-85f1-47e7-a606-cd0e1bfc00b1","year":2022},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.911700Z"},"links":{"cited_paper":"/paper/2210.08761","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:0d8273f067020de1465a326322c0398d53dece509396d53f46ff4ed7611a1ffa","observation_id":"e316b8c4-8043-4069-b090-0bcea0894846","resolution":{"observed_at":"2026-08-09T13:47:40.325726Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.07213","last_updated":"2024-08-13T21:52:04Z","snapshot_observed_at":"2026-08-09T14:34:26.630160Z","submitted_at":"2024-08-13T21:52:04Z","title":"Representation-space diffusion models for generating periodic materials","version":1},"cited_work":{"arxiv_id":"2408.07213","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.07213","snapshot_observed_at":"2026-08-09T13:47:40.310629Z","title":"Representation-space diffusion models for generating periodic materials","venue":"cond-mat.mtrl-sci","work_id":"eb869d8c-dbae-438e-b0eb-f7b25dc34a54","year":2024},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.915456Z"},"links":{"cited_paper":"/paper/2408.07213","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:8da2e3131df94a537894a3f080e78cbcdd8d8e8bb02702d553914ac2171d7e48","observation_id":"9b2ac735-d440-4eb5-ab15-d7ad12e7ab7d","resolution":{"observed_at":"2026-08-09T13:47:40.313904Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.918534Z","title":"Deep unsupervised learning using nonequilibrium thermodynamics","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.918534Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:81b3818e51d65852eb792cd98b6ef87189916779447629016dfd6a6de518b675","observation_id":"21144ffd-d92e-4acd-b948-1581e1d3ef95","resolution":{"observed_at":"2026-08-09T13:47:39.918534Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.921422Z","title":"Generative modeling by estimating gradients of the data distribution","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.921422Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:2a426ae398f114d7acd00e3777c3dd2958d73dfebb97dfe57ff0f1b001580a0d","observation_id":"748a3a0e-7e40-4113-ae38-8fe1fd5e8da5","resolution":{"observed_at":"2026-08-09T13:47:39.921422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.924078Z","title":"Improved techniques for training score-based generative models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.924078Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:ffca0e63905a08a6cc43663958e68ea569cf2afd2a0ea4813d8697b178ffa40f","observation_id":"c1321595-c4d1-449d-bba2-18614465f952","resolution":{"observed_at":"2026-08-09T13:47:39.924078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-09T13:47:39.926848Z","title":"Score-based generative modeling through stochastic differential equations","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.926848Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:7ac5904f2dd352a0ff8d9ecbecfc7bf126c73eb99bd22a9da43123dc810086d6","observation_id":"991de172-a3e9-48c6-a013-c64573f6d121","resolution":{"observed_at":"2026-08-09T13:47:39.926848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.049617Z","title":"Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole","venue":null,"work_id":"89185cbb-d044-4a79-bfab-a6d18c6f3d08","year":2020},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.930036Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:8c3f9657704655933261f770db890b59baadf3e295acbf4d3a3f2ed3d7b88d80","observation_id":"f8e15e20-eba4-472b-aa6b-af6fc73399d4","resolution":{"observed_at":"2026-08-09T13:47:41.052748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:39.932878Z","title":"Unified generative modeling of 3d molecules with bayesian flow networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.932878Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:9d3d75286430042d7c0bce939a788e866cbae11ff52cfd7a3f52ccaf39fa330a","observation_id":"7eb1f42e-2a40-4719-b55e-a1e493085d55","resolution":{"observed_at":"2026-08-09T13:47:39.932878Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.036220Z","title":"Equivariant flow matching with hybrid probability transport for 3d molecule generation","venue":null,"work_id":"8c9c99cb-cc18-4050-aa5f-edadade1d9e2","year":2024},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":99,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.935562Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:1ce644af5ffd0d1e8057bf00e91e7b0dd290aa97fd5b5cb7cea0327a18266507","observation_id":"7a89cfb7-2b8c-4c34-844d-9b36110663ec","resolution":{"observed_at":"2026-08-09T13:47:41.039377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T13:47:41.026477Z","title":"Bayesian inference with the von-mises-fisher distribution in 3d, 2017","venue":null,"work_id":"f5cd5166-11c5-4df1-a3ee-c28b785c89df","year":2017},"citing_paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation","version":1},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-08-09T13:47:39.938304Z"},"links":{"citing_paper":"/paper/2502.02016"},"observation_digest":"sha256:186266e1ea017b97805a316040fb6ce51532a612de4e7dda4fc2a74a23e2ffeb","observation_id":"0a3452c6-6f09-4aa5-b1b3-d5aefb749d26","resolution":{"observed_at":"2026-08-09T13:47:41.030068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.02016","last_updated":"2025-02-04T05:07:13Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T14:33:59.814755Z","submitted_at":"2025-02-04T05:07:13Z","title":"A Periodic Bayesian Flow for Material Generation"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":80,"verified_exact":5,"verified_fuzzy":15},"total_outbound_references":131},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 100 of 131 outbound references and 1 inbound Pith citation observation for arXiv:2502.02016."}