{"as_of":"2026-08-07T10:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0d59bfee7c73ea03c9d93124b13348051ddd582cf6e476b694a4df353310c0d8","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-12T02:31:03.871783Z","state":"measured"},{"denominator":56,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":56,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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":"2026-07-14T10:08:16.536665Z","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":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2607.03433","snapshot_observed_at":"2026-07-14T10:08:16.536665Z","title":"Harari, G., Zimmermann, Y ., Kulseng, O","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.10664","last_updated":"2026-07-12T09:17:57Z","snapshot_observed_at":"2026-07-31T00:00:41.541672Z","submitted_at":"2026-07-12T09:17:57Z","title":"Edge Cluster Expansion with Radial Rotary Attention for Interatomic Potentials","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-14T10:08:16.536665Z"},"links":{"cited_paper":"/paper/2607.03433","citing_paper":"/paper/2607.10664"},"observation_digest":"sha256:a5425ac9382337890884f4c7cd087cc92abe4f33adbbaf35431eea8cecd43b3e","observation_id":"c3097bfc-8f41-4a7e-a701-67deee5304a8","resolution":{"observed_at":"2026-07-14T10:08:16.536665Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2607.03433/citation-record","integrity":"/paper/2607.03433/integrity","json":"/paper/2607.03433/citation-record.json","paper":"/paper/2607.03433"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:347efa27ab5bfad831dfca8be35730a8a32a1a905809739560932c092fb75a98","observation_id":"cf34d767-14ed-44e7-8981-911af683ba91","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:dff0dff123f99d467b3ed0ab8dfe494c121e566d05db4483e8fdee0e77e538e3","observation_id":"d1ddd959-ee73-4581-9cfa-fe8741b4dd35","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Jacobs , author D","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:ee7d1fcd266d524496822fe751304b08bc86f22271eaff13f22c83f56af89795","observation_id":"36d4829b-98a6-427f-80c8-b7275a898414","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:ac2319366bb532b5086ed8cf73bf777946eb74cfd55af4ae98770c0d653461d0","observation_id":"fced5703-b5ea-4f15-862b-edf1951e6b8a","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.07327","last_updated":"2026-06-10T08:53:54Z","snapshot_observed_at":"2026-07-06T23:47:00.425715Z","submitted_at":"2026-06-05T14:45:06Z","title":"Six Open Questions in Machine-Learned Interatomic Potential Foundation Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.07327","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":"Creed , author T","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2606.07327","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:d5917a9f874f46179027248f53af65585c56004532652a68ec989c2ea36e4e01","observation_id":"bc0344f7-ae97-44e9-96a0-a40f7145951a","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.20217","last_updated":"2024-05-30T16:18:29Z","snapshot_observed_at":"2026-07-06T18:22:48.671656Z","submitted_at":"2024-05-30T16:18:29Z","title":"Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.20217","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":"Kaur , author F","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2405.20217","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:ee4421ed8958f42af430034920b9dfb68e17eb2f960418d6596d845f154edd13","observation_id":"23145205-e50e-419b-8c6d-dd5bbf9550d2","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.05652","last_updated":"2025-05-08T21:18:10Z","snapshot_observed_at":"2026-08-06T19:44:01.983413Z","submitted_at":"2025-05-08T21:18:10Z","title":"Fast and Fourier Features for Transfer Learning of Interatomic Potentials","version":1},"cited_work":{"arxiv_id":"2505.05652","doi":"10.48550/arxiv.2505.05652","metadata_source":"pith","pith_arxiv_id":"2505.05652","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Fast and Fourier Features for Transfer Learning of Interatomic Potentials","venue":"physics.comp-ph","work_id":"01721248-2e8d-4526-99e3-f2fb0489794f","year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2505.05652","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:fc8dde1f6dc1a9af7216e70bfb4526032b6e68a58a73d1d95a94a0866ce3682b","observation_id":"47a0d27e-9c07-4fca-8687-08d610a8386b","resolution":{"observed_at":"2026-07-12T02:38:25.444649Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-13T16:19:32.763493+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T16:19:32.763493+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.15582","last_updated":"2025-07-29T17:19:27Z","snapshot_observed_at":"2026-07-06T20:40:34.378362Z","submitted_at":"2025-02-21T16:45:05Z","title":"Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.15582","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":"Radova , author W","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2502.15582","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:db39d1cc72b3d59e092a43e22f421d1ac0fecc9ab9ffde5b5abaf252c6ca2a07","observation_id":"c78ec005-71a5-431e-a992-50b7774c72c2","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"10.1021/acs.jpclett.5c03801","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Hänseroth , author A","venue":"The Journal of Physical Chemistry Letters","work_id":"e797711e-49f2-48df-866f-afafd889dbb5","year":2026},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:1a94c6a5f37d887d59b22069665360b1b5ea0f61c06bd5b6cbf7889720b9b4a8","observation_id":"79dc4ac0-eb5b-43bd-8e6a-2122220330c0","resolution":{"observed_at":"2026-07-12T02:38:25.420302Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-13T16:19:33.335279+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T16:19:33.335279+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2606.12704","last_updated":"2026-06-10T21:46:04Z","snapshot_observed_at":"2026-08-01T18:38:10.365198Z","submitted_at":"2026-06-10T21:46:04Z","title":"Fine-tuning MLIP foundation models: strategies for accuracy and transferability","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.12704","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2606.12704","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:47f8887b660faedaf42c1c3c0f4b0bf314a9ba218161b3f6f782896a07b1a9f5","observation_id":"30c0099d-7150-4b8c-ad6a-234f2c7f5685","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Merchant , author S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:e71a087493530344d51d63ece56f277628d4e2f178c722ea03fd69cf3c160d57","observation_id":"d49986e1-63f6-4513-afae-4f86b82fcf1c","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.11568","last_updated":"2025-05-31T07:41:51Z","snapshot_observed_at":"2026-07-06T20:52:51.138710Z","submitted_at":"2025-03-14T16:37:23Z","title":"Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning","version":2},"cited_work":{"arxiv_id":"2503.11568","doi":"10.48550/arxiv.2503.11568","metadata_source":"pith","pith_arxiv_id":"2503.11568","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning","venue":"cond-mat.mtrl-sci","work_id":"1e58f47f-4784-4521-8c6e-84d055473c92","year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2503.11568","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:19529ce727b5e2f36afd13a2e55b75fb48b410bd7fddc0b8b858be222f496964","observation_id":"9f2cea6f-df1a-4708-ae1a-8efd6af42937","resolution":{"observed_at":"2026-07-12T02:38:25.384652Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-13T16:19:33.976579+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T16:19:33.976579+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.14110","last_updated":"2025-04-18T23:54:25Z","snapshot_observed_at":"2026-07-06T21:11:49.094540Z","submitted_at":"2025-04-18T23:54:25Z","title":"System of Agentic AI for the Discovery of Metal-Organic Frameworks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.14110","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2504.14110","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:89cbc53ac69c2e2c2943213cc6e53a237467a01122cb9c01a07cd44092c0bf6b","observation_id":"43e8d9d8-b5a6-45e7-844a-51dabd5d6d23","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Riebesell , author R","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:c0ee74834551c8ddb56e573d168c518000d3a1cf7e4ada69da9ade2781d17772","observation_id":"ca1fd881-4faa-4d21-85e3-cedbe63f41e7","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Chiang , author T","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:d960370217ad4d884d4926d34e8952dd419cba05adc79008f114f84e924d23c0","observation_id":"e04c60af-8a1e-4ab4-8c1a-e851106fdb23","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"10.1038/s41524-024-01259-w","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Choudhary , author D","venue":"npj Computational Materials","work_id":"7c3f4f1d-0bcf-46f2-8fcb-0f923d2ad8e9","year":2024},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:ca9eeb8709566f8abc1b66df977baf819d8487a6f3f7b4d902ff2b53e244d363","observation_id":"57745e04-17a6-4214-80ad-78e9fcbd553a","resolution":{"observed_at":"2026-07-12T02:38:25.455873Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-13T16:19:34.926557+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T16:19:34.926557+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-12T02:31:03.871783Z","title":"Peng , author C","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:0587846bfb7a2f200cd5d83a6748ff8efebc49cbda10c7666d3afb50fe53d3a9","observation_id":"ea018e90-9bbd-40aa-ba6b-ead6705e7dfa","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00755","last_updated":"2025-07-10T13:29:31Z","snapshot_observed_at":"2026-08-06T22:32:25.513099Z","submitted_at":"2024-08-01T17:57:24Z","title":"Thermal Conductivity Predictions with Foundation Atomistic Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00755","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":"P \\'o ta , author P","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2408.00755","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:9516d751aa80b2a56fdfbe88e3bf28c1fa1d2b2832669d1a5f5955da725d4c8a","observation_id":"3c0fce38-0897-44ab-b797-dce212fb41ec","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2025.102968","doi":"10.1016/j.xcrp.2025.102968","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moon , author U","venue":"Cell Reports Physical Science","work_id":"f05e2dec-b9af-464a-8a9e-9369c58981b0","year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:1d540e6ffc550a3f6a16f5882a3f2cbd24bde5d860863a4b39226c6236594806","observation_id":"a3c86de3-91f1-4039-a5a1-7e7b782a347d","resolution":{"observed_at":"2026-07-12T02:38:25.451505Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-13T16:19:35.56503+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T16:19:35.56503+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-12T02:31:03.871783Z","title":"Loew , author J","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:6f2e9556d3ef93382a5d6e3f7a70550a223b10b82038ea4c4970d18b4b8eefe4","observation_id":"24eb77c1-7645-48e4-94ae-00b2f059af01","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"10.1038/s41524-025-01872-3","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Kraß , author J","venue":"npj Computational Materials","work_id":"b3f25fef-9273-4f81-a97b-8f71a7090f56","year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:cb6cb168b68e9564f660227e4210d54cc0367ae960d681a5800e252955889c33","observation_id":"505db6a3-b361-4a28-b595-db95d17cb68b","resolution":{"observed_at":"2026-07-12T02:38:25.400111Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-13T16:19:35.921298+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T16:19:35.921298+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-12T02:31:03.871783Z","title":"Focassio , author L","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:3331c62a050a5d464aa31b3817d04f4a1fcc7480c66dfe74e04839a9d93f469b","observation_id":"a411abc9-858a-4759-b038-87965a114813","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Deng , author Y","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:aea030d5ae6ce62b3204a9bf3e27fe64cb4cb3536dad22bcef38f85019f4466f","observation_id":"f64694a7-90d5-4da9-8706-b78f3e9c028e","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"10.1021/acsmaterialslett.5c00093","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Wines \\ and\\ author K","venue":"ACS Materials Letters","work_id":"42b8a005-25d5-4906-af00-9290f7c14c85","year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:a090cfcabc2cf9fbf4624772019974c663d969f787f518ceaae26fd3ae29f68c","observation_id":"7bd8bc92-c95e-44dd-8b81-28ae660b3808","resolution":{"observed_at":"2026-07-12T02:38:25.488790Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-13T16:19:36.708141+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T16:19:36.708141+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-12T02:31:03.871783Z","title":"Deng , author P","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:88ef3594eb858832c6a4fcd84065d542599e17061849993be82fbf4d5b643a4c","observation_id":"d47123e1-e7ea-4089-8fb2-77cd9a2d86e0","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Schmidt , author T","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:c8b11e0be8b167b4b29a95e16f4e6edf40c70fca3c812af9c902cc1261399589","observation_id":"7679ab5f-5e2c-4a90-9880-f52b1b40fe04","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04967","last_updated":"2024-05-10T16:49:52Z","snapshot_observed_at":"2026-08-06T05:52:39.645210Z","submitted_at":"2024-05-08T11:13:30Z","title":"MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04967","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":"Yang , author C","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2405.04967","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:6d08624a6deac2d0a507c69452938fab4b3c4ce7b0b4dd3f1830d6ff96ea2094","observation_id":"4109671f-2d40-4328-aad6-95af7e92c0b1","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12771","last_updated":"2026-05-20T00:46:02Z","snapshot_observed_at":"2026-08-06T01:59:40.411635Z","submitted_at":"2024-10-16T17:48:34Z","title":"Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.12771","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":"Barroso-Luque , author M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2410.12771","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:e2c7b98bffc946a91e693416d39c3458f5cd22a207dfaf37d37d0a5b333b1557","observation_id":"14ac93f1-de5e-4ed3-ab87-388a9be075d8","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Malosso , author F","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:fba1ecd704bce0911e9c4fa6b7baf53bbce9bd0d65aee9342576d5f7cf883f2d","observation_id":"0cd457b7-7f55-45d1-be3a-9e1ea5b13e56","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:6e2227f5c2e9daa87591740104c7c04499390507518731d2634734e22fe0f4c0","observation_id":"5825113d-8c03-4f5c-9521-6a367066e14d","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"10.1021/acs.jctc.5c02006","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Journal of Chemical Theory and Computation","work_id":"ccaa3623-0d19-4218-ae01-0e1f97eb4b62","year":2026},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:d6323b2347da1cd83f5c95b759f82c89dbe47411e6904e4964adad74da6a8a46","observation_id":"c2415e6e-ecae-4d1c-9e58-e16f62a8328f","resolution":{"observed_at":"2026-07-12T02:38:25.351005Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-13T16:19:37.911943+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T16:19:37.911943+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.16195","last_updated":"2026-06-04T09:53:42Z","snapshot_observed_at":"2026-08-03T08:46:36.608036Z","submitted_at":"2026-01-22T18:46:58Z","title":"Pushing the limits of unconstrained machine-learned interatomic potentials","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.16195","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":"Bigi , author P","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2601.16195","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:0570d1f5e0625d2768145d7d9a4b2b8291c42d1d3ab135b1b1b65c75701eae93","observation_id":"41bfbb40-ddda-4696-a328-ea4c2802ae60","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":null,"venue":null,"work_id":null,"year":2044},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:189cf40572a4d79e9a1cad8610e60a0c60710ab14cf71c13d32a7945a79719cd","observation_id":"0b1f5cfb-98ad-4d80-9162-b891d092f763","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Blum , author R","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:b7e9da88893b854f43c3ccffcf08737555f9a114ad29ecd7ef082949d77f4064","observation_id":"7d4b71bf-9403-4c17-ae02-c8f051844948","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Litman , author V","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:1dd4ef18802bcdf76c9e38bce301e39da7a93e5c4b626ec5903910e6b4a8a436","observation_id":"9173a485-10fb-4787-a1cd-75f40dd9710a","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:a114ff97201dc918efc34154d607e27af3b724e93d62836da7e501fa84713d79","observation_id":"dea44dfc-5c0a-4336-bdd2-4796886313b3","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":null,"venue":null,"work_id":null,"year":1944},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:6e1943f53670a918bc0e8d7e793a6d86c8ba03fca1c35d71e6fbb7ce7ebdf826","observation_id":"554c1845-faa1-41a1-85d4-efc30c879d37","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Hjorth Larsen , author J","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:5ad8409819e5b882dc28c55c2ca7a682bd5e8908431cf6bb6330166a33868bc7","observation_id":"6ebd63c5-3f06-4ebc-9de4-da23b86b9031","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Batatia , author P","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:b737715a9d92a779698980add88b1d8eca30cb356d651b9f88f34c77e26b5ce3","observation_id":"166cf7d6-d25e-4102-bb59-dc7a2c1472ee","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Bochkarev , author Y","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:2005a3ab1ea42d009ebcfe2885f4c721013df48bd854a00a43818cbdd23f2f9c","observation_id":"a0c390f3-ca39-4998-acff-df073d41d0f2","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.22570","last_updated":"2024-10-29T22:20:14Z","snapshot_observed_at":"2026-07-06T19:41:55.010834Z","submitted_at":"2024-10-29T22:20:14Z","title":"Orb: A Fast, Scalable Neural Network Potential","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.22570","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":"Neumann , author J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2410.22570","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:5e1ffc46130cb0f9cbce5db6006bb57cbbab382a9fcfd3803938e640fe5c9f9a","observation_id":"21af87da-f271-4157-af57-c95e806181bb","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Lysogorskiy , author A","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:90c685c81671b2bd0b2c48f3ab82e284c765e3a93bcfbc872090ae046a911530","observation_id":"f311c922-f045-4e54-985c-32c7b1b5e9aa","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.06231","last_updated":"2025-04-10T18:13:52Z","snapshot_observed_at":"2026-07-06T21:06:17.499947Z","submitted_at":"2025-04-08T17:27:34Z","title":"Orb-v3: atomistic simulation at scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.06231","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":"Rhodes , author S","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2504.06231","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:32f88357bd904abfcf61160c62006f8bf087e525fe4c2f00064b9e1bf5c0a1ca","observation_id":"1d85575e-b4cd-48ec-9cd1-aafe687b7a28","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.12147","last_updated":"2025-04-23T05:37:35Z","snapshot_observed_at":"2026-07-06T20:38:06.085744Z","submitted_at":"2025-02-17T18:57:32Z","title":"Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.12147","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":"Fu , author B","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2502.12147","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:222b37487e2af7bc8ed96f0ef8e4ed37251f868e1d5368fb7a3f6e7a433de4bd","observation_id":"a175c471-bd63-42dc-a66f-9c648c8e5da6","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.16068","last_updated":"2025-04-22T17:47:01Z","snapshot_observed_at":"2026-07-06T21:13:09.093282Z","submitted_at":"2025-04-22T17:47:01Z","title":"High-performance training and inference for deep equivariant interatomic potentials","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.16068","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2504.16068","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:9e5507846b36fee81b82572937600231cf1ced84e7d53fbadbea1c26a38373d8","observation_id":"be17adeb-0c71-4b0a-a38b-3ae4fd8f530e","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.12059","last_updated":"2024-03-06T21:45:16Z","snapshot_observed_at":"2026-08-06T01:50:38.513001Z","submitted_at":"2023-06-21T07:01:38Z","title":"EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.12059","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":"\\ Liao , author B","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2306.12059","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:d64a47a17ac8d3ff790f3fa826170c1aa824c2a79818cd753a413373b278bced","observation_id":"f6ea4fd2-9def-4160-a3b0-de2ce7078e18","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Batatia , author C","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:a90ef84a4a7198501295a2bbecf281a6d9d131e5016c361296840bc0c25bc466","observation_id":"17d7f6d5-3e1f-4b21-9050-6b9ac578ccd2","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:12f3d30a9149fd0ce01eba79a3895716aa6f99c06022cf44ce0f7ce5b6fb2aa3","observation_id":"f25892ae-8e05-482b-b69e-0907c7a0482f","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Tuckerman ,\\ @noop en title Statistical Mechanics : Theory and Molecular Simulation \\ ( publisher OUP Oxford ,\\ year 2010 )\\ note google-Books-ID: Lo3Jqc0pgrcC NoStop","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:ed0ed522446e75d960097493317f4a1bc2b1cf233ef577917ed818064b216a17","observation_id":"598045ee-0b29-41f1-8a29-59093c8d70c6","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Kubo ,\\ title title Statistical- Mechanical Theory of Irreversible Processes","venue":null,"work_id":null,"year":1957},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:180f77194d2bda489d78b37da538a83d6e9b9237a71b5d2053576d2b67f18530","observation_id":"de5216ea-671a-4cc2-93b9-297253e1a612","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":null,"venue":null,"work_id":null,"year":1954},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:ad9916ec34f2652ce44b3b8267db75966810bba68834ad8b07d9836e4a009335","observation_id":"dee186a9-3c61-4e19-a000-d3b37ea4f90a","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":null,"venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:53a9f03b2bc5fbc6a6da692375b516ff5bbc7316e8e49ffe69e336a3c1530d3f","observation_id":"cfab0bdb-628a-4cef-8a80-83e53275fd2c","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"Grimme , author A","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:ac73461f737fda05f4df56ff8ed2198d1c55558aa805513de86eb30fb1a92a98","observation_id":"59567b96-9791-49e7-9635-cd53a8577291","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":"o rkman , author P. Blaha , author S. Bl \\","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:bf4c0737481ec83393b35ce1f0699dcbd392d24fb7a813d568a3b1c8e0214ce4","observation_id":"b9209fc8-54fc-46ce-9fe1-5790304c2503","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","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":"2026-07-12T02:31:03.871783Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:ec985adcf974208952396af108e0a6d284279429f29b4525ea182a3a6815bea9","observation_id":"8d2611e3-1dd7-41d0-adbe-315cc0ddd7ba","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","latest_version":1,"primary_category":"cond-mat.mtrl-sci","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":47,"verified_exact":8,"verified_fuzzy":0},"total_outbound_references":55},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2607.03433."}