{"as_of":"2026-08-09T15:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3e022357480d625ebe3ecd760337ec41fa4302902aa314967edd246218bc0168","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T19:56:13.172459Z","state":"measured"},{"denominator":45,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":45,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:40:14.438248Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T22:23:21.085903Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.09970","snapshot_observed_at":"2026-08-07T05:40:14.438248Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07401","last_updated":"2025-06-09T03:51:23Z","snapshot_observed_at":"2026-08-09T09:12:52.584394Z","submitted_at":"2025-06-09T03:51:23Z","title":"A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs)","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T05:40:14.438248Z"},"links":{"cited_paper":"/paper/2502.09970","citing_paper":"/paper/2506.07401"},"observation_digest":"sha256:38b88e4d82e7b652e805eb56f58497a16053367072a036ef086b5fab0763cbd8","observation_id":"a7ac4799-c016-4131-ad4f-fc6441bdb261","resolution":{"observed_at":"2026-08-07T05:40:14.438248Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"cited_work":{"arxiv_id":"2502.09970","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.09970","snapshot_observed_at":"2026-08-06T22:23:21.085903Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","venue":"cond-mat.mtrl-sci","work_id":"57a05f19-2caa-4e22-8bb4-07e19a642749","year":2025},"citing_paper":{"arxiv_id":"2506.21935","last_updated":"2025-08-22T05:20:08Z","snapshot_observed_at":"2026-08-09T10:17:09.559714Z","submitted_at":"2025-06-27T06:12:25Z","title":"Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T22:23:12.541333Z"},"links":{"cited_paper":"/paper/2502.09970","citing_paper":"/paper/2506.21935"},"observation_digest":"sha256:964d37213241f4268b5db86a31fd1930c27dc83a9080407f9d839d19a736a3c9","observation_id":"5c5893e1-0883-471b-a35c-1a58407ecbc0","resolution":{"observed_at":"2026-08-06T22:23:21.139496Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2502.09970/citation-record","integrity":"/paper/2502.09970/integrity","json":"/paper/2502.09970/citation-record.json","paper":"/paper/2502.09970"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:56:13.026307Z","title":"Solid -State lithium -ion bat tery electrolytes: Revolutionizing energy density and safety,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.026307Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:23d5a0db46304333a557e7fbabb6a48d8dd19e6e4c49f495b17af2bc7310e785","observation_id":"045e9d24-8c0c-4fe1-9b11-635772191a8c","resolution":{"observed_at":"2026-08-07T19:56:13.026307Z","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/s41578-019-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:56:13.444500Z","title":"Designing solid -state electrolytes for safe, energy -dense batteries,","venue":null,"work_id":"25fa3cba-6240-4f7e-9d1e-06b9c9bc1e50","year":2020},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.030535Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:9545691acc56ce05118cff983c28d007df58d3ce7ec364110f1cfdb4f7af1124","observation_id":"a4d04ba4-1827-4d55-8a22-3fc9b0481002","resolution":{"observed_at":"2026-08-07T19:56:13.448528Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"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":"2026-08-07T19:56:13.034220Z","title":"A solid future for batte ry development,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.034220Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:0910c24c7558c064ec3a945939979f66c14d5b2538ff45c10958ff6d40fed319","observation_id":"e6aad4d8-bec0-424e-9729-93b9ac21b9b2","resolution":{"observed_at":"2026-08-07T19:56:13.034220Z","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":"2026-08-07T19:56:13.037867Z","title":"Challenges in speeding up solid -state battery development,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.037867Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:771f0b2c2b583f64f13f17a4ff05f40a48081bd3368885cfd6f7bba332899ed7","observation_id":"66e284df-2a37-4d08-aac2-2a3bec9972f8","resolution":{"observed_at":"2026-08-07T19:56:13.037867Z","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":"2026-08-07T19:56:13.041590Z","title":"Fundamentals of inorganic solid - state electrolytes for batteries,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.041590Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:fb303b0788b460c57eb55dfdf44a860b34d7de4ea9bf60ef5f6720da96d89d2f","observation_id":"bc435fa6-7a0c-4362-b7b4-ede3b3118a12","resolution":{"observed_at":"2026-08-07T19:56:13.041590Z","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":"2026-08-07T19:56:13.045098Z","title":"Lithium superionic conductors with corner -sharing frameworks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.045098Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:a72268380b4dba0df2d3ba820cd3795240420c5e62b69b2ec258a34075ebe6b3","observation_id":"ae52cb8e-8a07-4fd2-aa6b-1be5a81a3e6d","resolution":{"observed_at":"2026-08-07T19:56:13.045098Z","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":"10.1126/science.add7138","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:56:13.404160Z","title":"A lithium superionic conductor for mil limeter-thick battery electrode,","venue":null,"work_id":"bec3b396-6adc-45ac-8727-50aa77a9ac70","year":2023},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.049022Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:cb5dde6017373552c4f849586849d7d11642e315dcdeffa58191a2e1fc7b4ddd","observation_id":"bd628569-00af-46d2-8f47-9f2415a96194","resolution":{"observed_at":"2026-08-07T19:56:13.408209Z","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":"10.1021/acsenergylett.9b02599","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:56:13.392707Z","title":"High-Voltage Superionic Halide Solid Electrolytes for All-Solid-State Li-Ion Batteries,","venue":null,"work_id":"832916b2-3f76-4a47-9840-cef98357e7f4","year":2020},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.052285Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:e662ff4e760264bf6f4ee90ddc5954b1679e23a0d7afdfb5a050e970dc9829f7","observation_id":"b8bc7ffc-a2f7-4d51-b6dd-e1f363d7e029","resolution":{"observed_at":"2026-08-07T19:56:13.396742Z","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":"2026-08-07T19:56:13.055679Z","title":"Prospects of halide-based all-solid-state batteries: From material design to practical application,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.055679Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:6d379b1d178ec8fb36814878553f221af3d267cc1cddaf6ff14bbbae134c9fc5","observation_id":"9e20ea61-aaec-4c9e-92f5-7dbf55da036d","resolution":{"observed_at":"2026-08-07T19:56:13.055679Z","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":"10.1126/science.abg7217","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:56:13.374522Z","title":"Carbon-free high-loading silicon anodes enabled by sulfide solid electrolytes,","venue":null,"work_id":"4111892f-100f-4693-9b71-4dc238be400e","year":2021},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.059047Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:db25a2a244fddaafc14c623ed8273a379103c4a80b77b068c070307b74af5eb9","observation_id":"3bf8278d-0b34-4fe8-a1f3-95d3b409ac09","resolution":{"observed_at":"2026-08-07T19:56:13.378211Z","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":"2026-08-07T19:56:13.062217Z","title":"The General AMBER Force Field (GAFF) Can Accurately Predict Thermodynamic and Transport Properties of Many Ionic Liquids,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.062217Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:33d29723c0b8c72d8926cf95b5579ae12b62d9914c4a7d46cc1fd56da580a2e9","observation_id":"b5b247d1-c3ea-4a28-9e4a-e75b933051b3","resolution":{"observed_at":"2026-08-07T19:56:13.062217Z","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":"2026-08-07T19:56:13.066035Z","title":"CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.066035Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:fbd07336d413088836dd09fae9313da8c6baa750004fa5bdf077f81b2479944e","observation_id":"b5e14426-ec45-44af-af2f-e8047622a448","resolution":{"observed_at":"2026-08-07T19:56:13.066035Z","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.jpcb.7b11548","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:56:13.349138Z","title":"Extension of the GROMOS 56a6CARBO/CARBO_R Force Field for Charged, Protonated, and Esterified Uronates,","venue":null,"work_id":"686d3ece-8356-4bf8-ae64-c2660c9b5de5","year":2018},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.069449Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:05508954db2c897d2eb95adc738501b806e4c3ea518f3c958fde19ac77584749","observation_id":"f53c1387-aa73-4d59-aa61-c2767ef3ef8e","resolution":{"observed_at":"2026-08-07T19:56:13.352908Z","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":"2026-08-07T19:56:13.072818Z","title":"Self-Consistent Equations Including Exchange and Correlation Effects,","venue":null,"work_id":null,"year":1965},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.072818Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:607a2e5b45ec66bbb304eab02c04b5aa088bbeefea2dae2de587bbbe4b778185","observation_id":"ceef1c68-61dd-406b-9530-26b85b7cfa04","resolution":{"observed_at":"2026-08-07T19:56:13.072818Z","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.jpca.8b12006","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:56:13.330840Z","title":"Anharmonic Molecular Mechanics: Ab Initio Based Morse Parametrizations for the Popular MM3 Force Field,","venue":null,"work_id":"a1a32340-fc22-4a77-8650-fd5047bfe3d0","year":2019},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.075976Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:a382cff00aefa9bac61c869f8ff2c35ebc92fd8f89264456b0afe77e4c11e0f5","observation_id":"bc114e64-fe77-4004-864d-40886d6a23e0","resolution":{"observed_at":"2026-08-07T19:56:13.334429Z","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":"2026-08-07T19:56:13.078906Z","title":"Perspective: Machine learning potentials for atomistic simulations,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.078906Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:91360832e577d370797abb1d01dc29098fc663be22cea74f825a8f5f55a56004","observation_id":"c1264814-ffd5-45aa-8691-a1e0ad02bf97","resolution":{"observed_at":"2026-08-07T19:56:13.078906Z","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.chemmater.7b05304","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:56:13.309583Z","title":"Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons,","venue":null,"work_id":"949d4db6-3a3d-44b9-a938-2d2143bc554c","year":2018},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.081985Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:0d9d8849ffd528c32d524cd37005e58380d3d779d0925ce1c7eebd01260de272","observation_id":"da91be15-acaf-4ad6-8b5a-4dfe1b65fb91","resolution":{"observed_at":"2026-08-07T19:56:13.315534Z","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":"2026-08-07T19:56:13.085063Z","title":"Recent advances and applications of machine learning in solid-state materials science,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.085063Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:b947cdb896b2a2f59c8be901018f959a26f59227f4acac67ed8989a5eb4b396a","observation_id":"412c4960-823d-4547-a7f1-4a600215916d","resolution":{"observed_at":"2026-08-07T19:56:13.085063Z","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":"2026-08-07T19:56:13.088115Z","title":"Machine Learning Force Fields,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.088115Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:597d59548f6ab79a16177b7927e8a93eccf12c80fa36c0c40d1a014ffe41f535","observation_id":"5a98fa2f-5ebf-4a90-a3e2-55031a3f641e","resolution":{"observed_at":"2026-08-07T19:56:13.088115Z","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":"2026-08-07T19:56:13.699761Z","title":"Riebesell, R","venue":null,"work_id":"ffe82b7a-a08f-4378-874b-c9488a7a6d0f","year":2023},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.091508Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:75760f1cc8b7b5cacb4c49fab27bf0b80aa520a74063e40f183f8f9ab662f834","observation_id":"2d1ae2d3-0dc7-49a9-a35d-3fee586a5b07","resolution":{"observed_at":"2026-08-07T19:56:13.702981Z","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":"2403.09549","last_updated":"2024-12-19T19:29:47Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:38:02Z","title":"Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09549","snapshot_observed_at":"2026-08-07T19:56:13.094722Z","title":"Generalizing Denoising to Non -Equilibrium Structures Improves Equivariant Force Fields,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.094722Z"},"links":{"cited_paper":"/paper/2403.09549","citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:7a195fd3517ff89ba02d4059105acba9f069d27c2514979190b4e0dad27165ac","observation_id":"730cd1a6-87ca-4ce4-a83b-1961e5bd8516","resolution":{"observed_at":"2026-08-07T19:56:13.094722Z","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":"2026-08-07T19:56:13.098184Z","title":"Systematic softening in universal machine learning intera tomic potentials,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.098184Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:6262dd4511a9e97f9b0b707e6e82cfbae4d1e78967023fa3ad38eac49b73ef30","observation_id":"ffa67377-1d9d-4d43-a91c-b97a0694e219","resolution":{"observed_at":"2026-08-07T19:56:13.098184Z","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":"2026-08-07T19:56:13.689451Z","title":"MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures,","venue":null,"work_id":"5738d3ef-223b-4121-bf0b-881c79aebdef","year":2024},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.101891Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:4f8c78ed83512430143b81d15bc50f1573fabcf31170ef96a5d470b1f01bf7f7","observation_id":"1fcf7ddc-5ecc-4a05-9f56-e27f09a13a30","resolution":{"observed_at":"2026-08-07T19:56:13.692794Z","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":"2410.12771","last_updated":"2026-05-20T00:46:02Z","snapshot_observed_at":"2026-08-08T19:09:45.735030Z","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":"2026-08-07T19:56:13.105502Z","title":"Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.105502Z"},"links":{"cited_paper":"/paper/2410.12771","citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:045ee05406da7b650745034f22ee8b321f6577d90b1b7f6d3ec094da5124cf88","observation_id":"ec28ce5c-6ab1-499d-89f3-c81977b8d18c","resolution":{"observed_at":"2026-08-07T19:56:13.105502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.07697","last_updated":"2023-01-26T10:07:20Z","snapshot_observed_at":"2026-08-07T06:56:25.158704Z","submitted_at":"2022-06-15T17:46:05Z","title":"MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.07697","snapshot_observed_at":"2026-08-07T19:56:13.109617Z","title":"MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.109617Z"},"links":{"cited_paper":"/paper/2206.07697","citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:63c7228921fbfec1a7f5e55dc7791229d0fec2f160d131f55c3083ab649b8dc2","observation_id":"54335129-ab51-4059-b618-a856bfc40a84","resolution":{"observed_at":"2026-08-07T19:56:13.109617Z","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":"2026-08-07T19:56:13.113518Z","title":"Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.113518Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:ced6422aa301a3402f470e6ec3c0f812fa31d05daf4e5e3b870eab7f125f7383","observation_id":"c0b58159-91e0-4460-b181-1b439e7ba31d","resolution":{"observed_at":"2026-08-07T19:56:13.113518Z","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":"2026-08-07T19:56:13.117383Z","title":"CHGNet as a pretrained universal neural network potential for charge -informed atomistic modelling,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.117383Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:b41f7af3b99302aced1587c700cacaa2e410ff888b5f9eb8a76d35c19552bb72","observation_id":"2c09d3fa-1ad1-4faa-8ad3-a0227ed6424c","resolution":{"observed_at":"2026-08-07T19:56:13.117383Z","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":"2026-08-07T19:56:13.121367Z","title":"A universal graph deep learning interatomic potential for the periodic table,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.121367Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:e1642d9b0e09e308ad7f9feb5f9495b2a61e95315da0c1306e443a3b4dea723b","observation_id":"6c3630c4-d0de-4e9a-98d3-e6a6b2b9ef13","resolution":{"observed_at":"2026-08-07T19:56:13.121367Z","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":"2026-08-07T19:56:13.125131Z","title":"Orb: A Fast, Scalable Neural Network Potential,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.125131Z"},"links":{"cited_paper":"/paper/2410.22570","citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:d1b6d9f79646444146d991e96ef798c387785680272be71aaf7f36d7a156be81","observation_id":"b2b76f11-8754-4a63-80b2-217e158ec763","resolution":{"observed_at":"2026-08-07T19:56:13.125131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.03641","last_updated":"2017-12-31T03:48:06Z","snapshot_observed_at":"2026-07-06T06:13:41.923764Z","submitted_at":"2017-12-11T04:16:43Z","title":"DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics","version":2},"cited_work":{"arxiv_id":"1712.03641","doi":null,"metadata_source":"pith","pith_arxiv_id":"1712.03641","snapshot_observed_at":"2026-08-07T19:56:13.533562Z","title":"DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics","venue":"physics.comp-ph","work_id":"17404dc7-2b54-436e-bebb-022f89977f31","year":2017},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.129013Z"},"links":{"cited_paper":"/paper/1712.03641","citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:d90a5f279d8392026eb94e4e0d7edcc4769afbb0f72f0d4aa17e029cb8719584","observation_id":"0e35dc7d-b700-407e-95c2-cffaf110710e","resolution":{"observed_at":"2026-08-07T19:56:13.537427Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:56:13.678834Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Phonons,","venue":null,"work_id":"aaf2868d-8cb9-4bb0-abf5-451ab5ff7740","year":2024},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.132653Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:9926f3198816fe90435de5ef7035f7f6c62ed7571970b2f253ea7756573c907b","observation_id":"357dc729-4da0-4158-a66d-f3bee9d42306","resolution":{"observed_at":"2026-08-07T19:56:13.682546Z","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":"2026-08-07T19:56:13.668440Z","title":"Neural Message Passing for Quantum Chemistry,","venue":null,"work_id":"9a39b933-a696-4741-9814-10a4bf294656","year":2017},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.135722Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:2fbe178343bdf1f4b996d3265945d4c0d88a7c244057fe33a88b3bdd1b339fb0","observation_id":"665106bb-0e34-44fe-9b30-8c60e2dd4476","resolution":{"observed_at":"2026-08-07T19:56:13.671805Z","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":"2026-08-07T19:56:13.138924Z","title":"Generalized Neural -Network Representation of High -Dimensional Potential- Energy Surfaces,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.138924Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:1e436c6c0e5fa4785cc7f1c3bb00557c650b7fce182b54b93c62b57b20980731","observation_id":"dba76fa4-8df2-4b7a-9968-642b5688d954","resolution":{"observed_at":"2026-08-07T19:56:13.138924Z","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":"2026-08-07T19:56:13.141992Z","title":"E(3) -equivariant graph neural networks for data -efficient and accurate interatomic potentials,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.141992Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:58975b4f3f49c352e3ac81b14a460649a96afdf23e6a1e1ac5c68fb76f64c07f","observation_id":"1bc1f633-f47f-47e5-9e23-b3a656abaa33","resolution":{"observed_at":"2026-08-07T19:56:13.141992Z","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":"2026-08-07T19:56:13.657821Z","title":"The atomic simulation environment -a Python library for working with atoms,","venue":null,"work_id":"86864b81-5a84-4bd3-8073-f058d0b8fdc6","year":2017},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.145235Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:b11bac2a46febb4783aadf046a0365af548c5e75f7a52eebabeeb82fce611971","observation_id":"e399c8a2-0842-49bd-bacd-57786aea9803","resolution":{"observed_at":"2026-08-07T19:56:13.661287Z","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":"2020.10958","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:56:13.514727Z","title":"Assessment and optimization of the fast inertial relaxation engine (fire) for energy minimization in atomistic simulations and its implementation in lammps,","venue":null,"work_id":"bdb3a675-071a-437d-b888-3cab1bd1b7a2","year":2020},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.148577Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:566fc141c431708f4e995ceb315f82e01984612adab75b743d2077862926c3da","observation_id":"994c0a61-5e58-42a1-92b5-41723e9ad8ed","resolution":{"observed_at":"2026-08-07T19:56:13.520613Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"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":"2026-08-07T19:56:13.151750Z","title":"Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.151750Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:e22ede030dfd986c43a5e7f9ebfdd864c81a00dfdfe008a30a7b7bd642ff70a1","observation_id":"e9f824d0-4c0a-4dd4-892c-5d13e63a3aca","resolution":{"observed_at":"2026-08-07T19:56:13.151750Z","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":"2026-08-07T19:56:13.155050Z","title":"Active learning of uniformly accurate interatomic potentials for materials simulation,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.155050Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:2b1830f46dfa919f41e6faf26a947951a74a0cdfe4a05f677061dcc74b0e7f91","observation_id":"59eb86db-72e4-442e-ac4d-8ef6c0522fe7","resolution":{"observed_at":"2026-08-07T19:56:13.155050Z","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":"2026-08-07T19:56:13.158964Z","title":"Ab initio molecular dynamics: Concepts, recent developments, and future trends,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.158964Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:45400ad433c4cc54a7eb65893a7ab8cbc18d9b49af5005a95e355f3b60e2b79a","observation_id":"999e34e3-4a65-4d46-b809-b5699ae37c25","resolution":{"observed_at":"2026-08-07T19:56:13.158964Z","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":"2026-08-07T19:56:13.162647Z","title":"Projector augmented-wave method,","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.162647Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:10b1dda87ab7ffde8c3eb23095e11149b9fc1777cd1acc5693c74013d6f7e4c4","observation_id":"a5195c8f-7c6e-4f5e-a1f9-68fe2fe26aba","resolution":{"observed_at":"2026-08-07T19:56:13.162647Z","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":"2026-08-07T19:56:13.165841Z","title":"Robust training of machine learning interat omic potentials with dimensionality reduction and stratified sampling,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.165841Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:f0b9931735f95ea5726f59d8074ebc47f819374c054fc640e1ef0f401d535818","observation_id":"b7f85376-8cb9-4749-b653-e7f5bbf0f015","resolution":{"observed_at":"2026-08-07T19:56:13.165841Z","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":"2026-08-07T19:56:13.169255Z","title":"Data-Driven First-Principles Methods for the Study and Design of Alkali Superionic Conductors,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.169255Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:c4300d6749213f7c6bfb9c5cb160cd616c7aac952495631901ead6e231e22978","observation_id":"62ab6198-4d77-4da2-9a77-45b3d796bb04","resolution":{"observed_at":"2026-08-07T19:56:13.169255Z","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":"2026-08-07T19:56:13.172459Z","title":"Accelerating Computational Materials Discovery with Machine Learn ing and Cloud High - Performance Computing: from Large-Scale Screening to Experimental Validation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T19:56:13.172459Z"},"links":{"citing_paper":"/paper/2502.09970"},"observation_digest":"sha256:c2dc1db49724b91f40b9f7776904eca8f1673e6b6ab35fc7120d16f43c7dfbee","observation_id":"33282c94-bf43-4f4d-820a-b794d59125f4","resolution":{"observed_at":"2026-08-07T19:56:13.172459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.09970","last_updated":"2025-02-14T07:55:53Z","latest_version":1,"primary_category":"cond-mat.mtrl-sci","snapshot_observed_at":"2026-08-07T19:49:40.488405Z","submitted_at":"2025-02-14T07:55:53Z","title":"Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":29,"verified_exact":7,"verified_fuzzy":5},"total_outbound_references":43},"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 9 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 inbound Pith citation observations for arXiv:2502.09970."}