{"as_of":"2026-08-04T23:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f42c16e90cef68f183f3253a602435451c3ead284f5f24be29619a68091564a1","coverage":[{"denominator":66,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":66,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-07T06:32:25.205347Z","state":"measured"},{"denominator":68,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":68,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+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":"2026-07-11T16:17:26.963678Z","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":"2026-07-02T00:16:24.592347Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"cited_work":{"arxiv_id":"2604.27685","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.27685","snapshot_observed_at":"2026-07-02T00:16:24.592347Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","venue":"cond-mat.mtrl-sci","work_id":"726abbe8-5b2b-427e-a447-2f9ee1463ab5","year":2026},"citing_paper":{"arxiv_id":"2606.02507","last_updated":"2026-06-01T17:20:55Z","snapshot_observed_at":"2026-07-06T23:42:53.514946Z","submitted_at":"2026-06-01T17:20:55Z","title":"Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-06-28T13:30:35.790162Z"},"links":{"cited_paper":"/paper/2604.27685","citing_paper":"/paper/2606.02507"},"observation_digest":"sha256:e74fa462f2896d8a5f617652dd9ff9ea24056bc32b2d4b7263cdc4bafddba93c","observation_id":"6aed28d7-dde4-4962-9cb8-8e26e537e649","resolution":{"observed_at":"2026-07-02T00:16:24.594057Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.27685","snapshot_observed_at":"2026-07-11T16:17:26.963678Z","title":"doi:10.48550/arXiv.2604.27685 , abstract =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04622","last_updated":"2026-07-06T03:04:15Z","snapshot_observed_at":"2026-08-03T18:26:46.984236Z","submitted_at":"2026-07-06T03:04:15Z","title":"VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-07-11T16:17:26.963678Z"},"links":{"cited_paper":"/paper/2604.27685","citing_paper":"/paper/2607.04622"},"observation_digest":"sha256:f476b312908250b23e9eed62176eede95803050e59e0ed91a4f0aee60ec9fd4e","observation_id":"2e04e8e6-bd5f-4a29-8edf-f094cafb9e8f","resolution":{"observed_at":"2026-07-11T16:17:26.963678Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2604.27685/citation-record","integrity":"/paper/2604.27685/integrity","json":"/paper/2604.27685/citation-record.json","paper":"/paper/2604.27685"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"ba79c9d2-575b-4cb6-b95b-de8694e732d0","year":2024},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:4cd7d72ec2f0436f6d88d2cd94cc6062ce82a7a232f3573722d5fac8ff76fb63","observation_id":"6ab64095-2aa8-4477-9808-d8dcbfedef5b","resolution":{"observed_at":"2026-05-27T10:03:57.082802Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"L., Takeuchi, I","venue":null,"work_id":"21ec6c08-6274-4d65-9409-fc818d50669a","year":2022},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:b23167cbb051f9449000ccae1dcc129c2c8493b5ef0a6c15b00b930885720fd7","observation_id":"ffec3629-48ce-43ab-be9d-475594aaa5ba","resolution":{"observed_at":"2026-05-27T10:03:57.086472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4a319194-56c3-45a1-bd8e-2081dec8be75","year":2024},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:74c4d5d2f11a074f886ac7fd884b896586e796f673e050e923acd930dca11a21","observation_id":"c9d6acd5-6cb0-4ccf-9e4d-fabff7b6d2c9","resolution":{"observed_at":"2026-05-27T10:03:57.170585Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"2a5bdb27-3708-445b-9e3e-990a47477f27","year":2022},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:15a555704eda7182b86aa28e73fa10c814816530d7d2a45afe6ab4433872ec2a","observation_id":"9e10c1af-1813-4ba4-84eb-450b16c5115a","resolution":{"observed_at":"2026-05-27T10:03:57.079162Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"6761ddd0-f28f-45c4-aaa9-5c7ecb4a78b9","year":2018},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:5a255903f6d37c64feee3f754dc1e6508b6ee0cf737847657d3844bc6a2d403c","observation_id":"04bff199-3676-44e0-88dd-a5279af0eae7","resolution":{"observed_at":"2026-05-27T10:03:57.202713Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.17946","last_updated":"2025-02-25T08:08:23Z","snapshot_observed_at":"2026-07-06T20:42:14.536063Z","submitted_at":"2025-02-25T08:08:23Z","title":"High-throughput computational screening of Heusler compounds with phonon considerations for enhanced material discovery","version":1},"cited_work":{"arxiv_id":"2502.17946","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.17946","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"& Tadano, T","venue":null,"work_id":"260f00e6-e085-4e76-9ba9-62384263d351","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"cited_paper":"/paper/2502.17946","citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:acb3f17ce68ec53e0cd8d5f7e6a1bd0ec79350b165da79eec1dd8de7ea394c43","observation_id":"a5474796-6e59-48a0-af4e-497abc7f0d8a","resolution":{"observed_at":"2026-05-12T10:21:29.058087Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"D., Xia, Y","venue":null,"work_id":"2a470dba-8016-4471-befa-663664edd2f0","year":2023},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:c3afd13293cac5460741a07c960251fcc6fd5ac1b72eebfe10428f12bb3ea6fb","observation_id":"6e717dda-5799-40f0-a488-289fae8db676","resolution":{"observed_at":"2026-05-27T10:03:57.231049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"& Mizukami, W","venue":null,"work_id":"2df20e4c-9c92-4d2b-b14c-2965f6117abe","year":2024},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:2bf08fdec5223b62a53bb48327ec1645949e4e1e56779d0a7fadab80996b64ac","observation_id":"5b3fb7e9-c09a-451e-be9d-1e888109a826","resolution":{"observed_at":"2026-05-27T10:03:57.178484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"04f75321-3fd3-4a20-91fe-f6a80a24a4d6","year":2024},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:0b30d6d73091d2c8d63ced40926887fe70b8d4872a412cae8071c3d1521f93a1","observation_id":"0d1a43eb-cb1b-422f-85c6-d1bd23340547","resolution":{"observed_at":"2026-05-27T10:03:57.068127Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"f7201642-29eb-4125-b260-5654571cfc4b","year":2022},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:024ed03b973b2689615e36e83b6507c58a73310bdfed3253c2073f7069e30464","observation_id":"919b187b-c157-4faf-b015-6d8cb75855ca","resolution":{"observed_at":"2026-05-27T10:03:57.100997Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"P., Simm, G","venue":null,"work_id":"e143526a-08d0-4bf8-b782-308076a8e093","year":2022},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:ed44a17b94ec669c7b2dfa0caff59dd298e354c1bd1b30d4f8f82fc0d6dfc3c2","observation_id":"f13b1b23-f991-4b3e-9bf2-ab1866c6e8e0","resolution":{"observed_at":"2026-05-27T10:03:57.224426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"& Jung, Y","venue":null,"work_id":"0dd45157-bcd4-420b-b5e6-8013d8855d51","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:0f74b7eecd54e1c515d11c026f2837b0618c38fefc66db5754c81c92b64bba6a","observation_id":"f418b74a-fa3d-4b56-a1cb-07543212a044","resolution":{"observed_at":"2026-05-27T10:03:57.064718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"f1ce8c48-693e-459b-96d1-62d90d92d89c","year":2023},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:35e5bda9edd069101cd357a28d027ab882848d75498189f9dd516ef1b3541b6a","observation_id":"1b188313-8069-4a49-9938-b2225a08c033","resolution":{"observed_at":"2026-05-27T10:03:57.075389Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2502.12147","doi":"10.48550/arxiv.2502.12147","metadata_source":"arxiv_reference","pith_arxiv_id":"2502.12147","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Wood, Luis Barroso-Luque, Daniel S","venue":null,"work_id":"f9e076b9-c8f0-4699-8e28-83d01b214b7c","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"cited_paper":"/paper/2502.12147","citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:a3788fd90fbc7d92f84550fd2f931d1d812fef5c283c196d45039ebc1033aa8a","observation_id":"71437c47-5562-46cf-aa94-150397cf4aba","resolution":{"observed_at":"2026-05-12T10:21:29.017890Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2506.23971","doi":"10.48550/arxiv.2506.23971","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Uma: A family of universal models for atoms","venue":null,"work_id":"bfb2db48-c290-47d3-9daf-0e3b46fc7675","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:ea4cd224e63e4db3a93b2a7f5933eaf5e79b8eb1eb89179861e647ae2201bbd8","observation_id":"4898582a-4e67-4eaf-915b-c30ca437d66c","resolution":{"observed_at":"2026-05-12T10:21:29.085177Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-07-14T05:21:13.841225+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T05:21:13.841225+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"S., Ghorbani, K., Hatam-Lee, S","venue":null,"work_id":"9fd1f0d3-4368-4263-b8ca-b9f1847c1b62","year":2021},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:1f1f2666b43051e113b51946d9ed849a59054aaf630817cefa8e8f46c9cc47df","observation_id":"b0e10aec-ca8e-4219-810f-fe15f4a2777b","resolution":{"observed_at":"2026-05-27T10:03:57.173844Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"& Marques, M","venue":null,"work_id":"dded8724-1370-46c5-b5cd-72eef5703e73","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:4961c10b946f29f0d8d5d8b8af09149de54da899a4d240626d65d38d3e560a23","observation_id":"481e46f2-ba95-4b05-bfc2-5266c7c35ce8","resolution":{"observed_at":"2026-05-27T10:03:57.057904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"& Cao, B","venue":null,"work_id":"18e0c6ec-b9b7-404d-9593-8d3e9f7e1aca","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:4d101d813418f48a11d884b81ad0317f714e3f8beaa371014de17dd7e51fd53c","observation_id":"23dc3032-208b-47b9-9d68-1a29a159dd66","resolution":{"observed_at":"2026-05-27T10:03:57.220655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00096","last_updated":"2025-09-04T13:50:21Z","snapshot_observed_at":"2026-08-04T22:35:15.439821Z","submitted_at":"2023-12-29T23:08:59Z","title":"A foundation model for atomistic materials chemistry","version":3},"cited_work":{"arxiv_id":"2401.00096","doi":"10.48550/arxiv.2401.00096","metadata_source":"pith","pith_arxiv_id":"2401.00096","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A foundation model for atomistic materials chemistry","venue":"physics.chem-ph","work_id":"29bfbd6f-762e-4448-8f07-4f24bf553e51","year":2023},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"cited_paper":"/paper/2401.00096","citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:c8c952ce9c04bea22501bcda10252adb8fb1c9a537dfcbac40ecb31c12d0a6cb","observation_id":"1d759406-b888-45bc-8c30-866f752b8233","resolution":{"observed_at":"2026-05-18T10:16:16.878114Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-05-19T23:52:00.679912+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-19T23:52:00.679912+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"& Cheng, Y","venue":null,"work_id":"ed9a9e15-4c02-4762-bcc9-77478f4d4a51","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:a02e21ee9351401d610b573e83f8c4a7bd3c76370675189f4d78f8f29275cb8f","observation_id":"81324aab-352e-4c50-a6a0-5acdb65e7504","resolution":{"observed_at":"2026-05-27T10:03:57.048300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2502.15582","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.15582","snapshot_observed_at":"2026-07-03T22:08:59.317437Z","title":"https://arxiv.org/abs/2502.15582","venue":null,"work_id":"2ccd8fdc-3484-4fbb-b4dc-2d9306402c2b","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"cited_paper":"/paper/2502.15582","citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:6792cb90bfd837a0b4e245ee5e2cb6b67cbab3dbeef62f7cb6de6dfe03651dcb","observation_id":"d71aa4b7-e999-4bee-9aa2-8ced49f11250","resolution":{"observed_at":"2026-05-12T10:21:29.049689Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.21935","last_updated":"2025-08-22T05:20:08Z","snapshot_observed_at":"2026-08-02T07:05:55.370320Z","submitted_at":"2025-06-27T06:12:25Z","title":"Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications","version":2},"cited_work":{"arxiv_id":"2506.21935","doi":"10.48550/arxiv.2506.21935","metadata_source":"arxiv_reference","pith_arxiv_id":"2506.21935","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"arXiv preprint arXiv:2506.21935 , year=","venue":null,"work_id":"2ce361b0-942f-48d4-8070-ef47969d4f1d","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"cited_paper":"/paper/2506.21935","citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:6389a9f8318da6bce222fa922b699f75a0f5652b34f87f73bfff4e293c7df5f9","observation_id":"82c1b4c5-ab0a-4fc2-bf97-35a3cbb58ad7","resolution":{"observed_at":"2026-05-12T10:21:29.000123Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2601.21393","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"S., Trybel, F., Faber, F","venue":null,"work_id":"fceb2626-55b6-45f4-af21-c9b7ac7df97c","year":2026},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:5f961414b4343edd89bd05b0f973d007ba01d622c59c41ca363bf1dfb398b66a","observation_id":"ecbb9d3d-dfd4-43ba-9f63-9a2f7e5a99c2","resolution":{"observed_at":"2026-05-12T10:21:29.044935Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.02582","last_updated":"2025-07-11T15:35:20Z","snapshot_observed_at":"2026-07-06T20:31:08.252721Z","submitted_at":"2025-02-04T18:56:47Z","title":"Open Materials Generation with Stochastic Interpolants","version":2},"cited_work":{"arxiv_id":"2502.02582","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.02582","snapshot_observed_at":"2026-07-04T08:49:42.337169Z","title":"org/abs/2502.02582","venue":null,"work_id":"fc6550a7-0648-4289-a690-caa00412451d","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"cited_paper":"/paper/2502.02582","citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:acd2562faedf55b9dbb20774c9a383664c8faf2480ea890168fd8c8163115b56","observation_id":"b38fd527-d66e-4d5f-8c79-6049df7afa9a","resolution":{"observed_at":"2026-05-12T10:21:29.029744Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2512.21227","last_updated":"2026-06-11T04:08:12Z","snapshot_observed_at":"2026-08-03T14:15:44.007858Z","submitted_at":"2025-12-24T15:07:36Z","title":"PhononBench:A Large-Scale Phonon-Based Benchmark for Dynamical Stability in Crystal Generation","version":3},"cited_work":{"arxiv_id":"2512.21227","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2512.21227","snapshot_observed_at":"2026-06-12T02:08:23.457247Z","title":"& Lu, Z.-Y","venue":null,"work_id":"746c94c7-a449-4337-9580-b99e21279f5b","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"cited_paper":"/paper/2512.21227","citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:ac5247b0b49787045b7e809ff2de5793dca110cf5f0a8a0ad0c05232e546013c","observation_id":"05e93faf-1b9c-475c-9d40-51d1bbcc6aab","resolution":{"observed_at":"2026-06-12T02:08:23.457247Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"& Tanaka, I","venue":null,"work_id":"2ba0ffbc-bcef-4e52-8a4b-1c7e0d215486","year":2013},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:5b2ce4bec53fe7be114dd23612a48ea490aea23da54286ec7a84bb91ed0b658d","observation_id":"3bec49aa-119e-4bff-a8c5-7e086098870d","resolution":{"observed_at":"2026-05-27T10:03:57.051761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"P., Simm, G., Ortner, C","venue":null,"work_id":"b514af5f-817f-4c06-8a78-7d10cea1317b","year":2024},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:4a6be759af6c6b0eb1ab052f43a88ce18fa106c8d93cdc282d7a87183886cb7d","observation_id":"75da64e1-4d5c-4838-9f2e-8641504ad255","resolution":{"observed_at":"2026-05-27T10:03:57.054794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"& Rabe, K","venue":null,"work_id":"33eb9819-41bd-4a0a-81c8-60a5794cde14","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:628313f2f8020782e088568bfa579a1d1b1dfa10803b394700fcabce4c037645","observation_id":"f531e9f1-e73d-462b-90ca-caabed894ac7","resolution":{"observed_at":"2026-05-27T10:03:57.071364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"854c4d10-0977-4e3d-9735-21970e7feb1f","year":2020},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:008d1017d6e0da5cc153fb827116c37d5c4a3611caf70736736e9eac76d5f110","observation_id":"b7a85054-4747-48cf-a21a-db1bb4142d58","resolution":{"observed_at":"2026-05-27T10:03:57.061101Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"A., Bentria, B., Dahame, T","venue":null,"work_id":"531c234d-597f-451d-b257-d52d5a78908a","year":2020},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:8a66893734e24b7f08a11defe5d3a5237e55b7e4153ef37b8d924ddcfe9e81a4","observation_id":"f1211cdb-b9a0-43ae-8635-ab98ba52d915","resolution":{"observed_at":"2026-05-27T10:03:57.090073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"& Navrotsky, A","venue":null,"work_id":"7b0b4bfe-e6fc-4e47-823f-789685ecd4ad","year":2019},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:38f230bf33ffcb00561e1436669b650acb2e475667acea61fd44a833a06b468f","observation_id":"05d83e22-297d-4def-b5b3-87be2cdd3132","resolution":{"observed_at":"2026-05-27T10:03:57.096645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"63a9e3d5-5c04-4bef-a265-643f53250dc0","year":2024},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:a6b2fbd1196426e836db7357a0c1d01ee3171fb9c4b2d56b13002ae218f7a361","observation_id":"9d6fdac0-3343-457c-a239-d22210fa2872","resolution":{"observed_at":"2026-05-27T10:03:57.216944Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"dfd454a2-18f1-4731-8b7c-b85b4be85025","year":2022},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:be103a720c4f0d45f9ae0232566a77f9696c24a59cc775327c0df89b410bf5f1","observation_id":"0cd8ae86-0385-4259-951b-811388255191","resolution":{"observed_at":"2026-05-27T10:03:57.195827Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2602.07295","last_updated":"2026-05-28T20:40:32Z","snapshot_observed_at":"2026-08-03T03:42:30.868857Z","submitted_at":"2026-02-07T01:04:47Z","title":"The impact of spurious imaginary phonon modes on thermal properties of Metal-organic Frameworks","version":3},"cited_work":{"arxiv_id":"2602.07295","doi":null,"metadata_source":"pith","pith_arxiv_id":"2602.07295","snapshot_observed_at":"2026-07-01T19:56:11.523262Z","title":"The impact of spurious imaginary phonon modes on thermal properties of Metal-organic Frameworks","venue":"cond-mat.mtrl-sci","work_id":"e82d5800-0240-4147-8252-eaaeaf3668f4","year":2026},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"cited_paper":"/paper/2602.07295","citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:df05ade6b098c72453537f23b9685f0e9e5bed9e96eac5809d711967f0b94990","observation_id":"3cf8cf74-e149-473b-bb97-ced380f955ed","resolution":{"observed_at":"2026-06-01T02:02:27.027094Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2602.06893","last_updated":"2026-06-03T14:18:23Z","snapshot_observed_at":"2026-08-03T03:50:01.521300Z","submitted_at":"2026-02-06T17:27:29Z","title":"From Symmetry to Stability: Structural and Electronic Transformation in Cs$_2$KInI$_6$","version":2},"cited_work":{"arxiv_id":"2602.06893","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2602.06893","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A., Rignanese, G.-M","venue":null,"work_id":"5839bd88-0869-4c4f-ab90-3386f1e5a002","year":2026},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"cited_paper":"/paper/2602.06893","citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:2e322aa8db5f99770323f89b310edf476e57226e1184bbac5514e17eadf54987","observation_id":"3cfd4be9-dbb8-4b43-8708-013ff9edb9f7","resolution":{"observed_at":"2026-06-04T02:07:44.901196Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"& Yin, J","venue":null,"work_id":"0cb76add-a3b0-483a-b972-885769698b0e","year":2026},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:84585d98e07ff2abc60bdaf05443627e353e42e7d6ce345faa661307889bc410","observation_id":"5a7c6baf-5e5b-4841-bf8a-88b2f7457a46","resolution":{"observed_at":"2026-05-27T10:03:57.199049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"J.et al.Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains.Small20, 2303565 (2024)","venue":null,"work_id":"d29caf92-c783-43d1-b5fd-4ae99cc40ffa","year":2024},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:a7a8230f656577f347cf93cddc140eae1f54d9b693b5ebd3af7f89f095515314","observation_id":"0f6f8d34-ebe4-4083-a822-3f0af9c262c7","resolution":{"observed_at":"2026-05-27T10:03:57.182029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"5b96c854-027e-4236-965f-26b650bb9bce","year":2022},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:f38cee4ac29e2e7f6291d44eecdbdc9d618005e20416341ab76592d96ded345d","observation_id":"3eb1845d-eac3-4d37-afa5-4a4ec0a51cf8","resolution":{"observed_at":"2026-05-27T10:03:57.185366Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"& Sleight, A","venue":null,"work_id":"db906d9d-d777-4202-8c46-a58406245cce","year":1980},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:4d3390dc047ac9eaa9d06b8e06bc2a7531a3dcb52e692dd5a94b7e69c5992c3f","observation_id":"54cbfd03-0b0c-4d40-a2c9-f89b75ef009f","resolution":{"observed_at":"2026-05-27T10:03:57.189188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"W.et al.An Exhaustive Symmetry Approach to Structure Determination: Phase Transitions in Bi 2 Sn2 O7.Journal of the American Chemical Society138, 8031–8042 (2016)","venue":null,"work_id":"7c346fd4-d642-4d4d-a6d7-ed8bdcd9bba8","year":2016},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:635c59176109fd2ecec165381b480c9f972741dc055761e906143de9b3af242a","observation_id":"66a76346-b903-41e3-96a4-c9e704de6523","resolution":{"observed_at":"2026-05-27T10:03:57.192652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Z.et al.A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons.Advanced Intelligent Discoverye202500075 (2025)","venue":null,"work_id":"bcb67e9f-b4d5-4ae8-a179-f583bd91bfd6","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:15dcc03f7ca016ee05227f31a230c051e28829103e5c3a1d3fa3ebc90f3055bd","observation_id":"1ca4bb5c-3bfa-4310-b56d-e92acfd9dac6","resolution":{"observed_at":"2026-05-27T10:03:57.210187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2601.22009","doi":"10.48550/arxiv.2601.22009","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"A., Vandergheynst, P","venue":null,"work_id":"e93ad3c1-891b-43b2-9a68-51a24d2f225d","year":2026},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:2db9f153b6a4ebe72b0c48b550f52eb4372af30d88a02761368c86769cafc57b","observation_id":"6a278bd9-e8a9-46be-906c-505d9061e659","resolution":{"observed_at":"2026-05-12T10:21:29.006577Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"f8cca7cb-72ef-4a8e-a4b6-e9b66454af93","year":1976},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:a934ae6d96394c9facc1ebf4e18ce131f88ad7077b5c621aa4f509bcfc7ff6b0","observation_id":"13e2685b-ab28-47d0-8241-44925811d50e","resolution":{"observed_at":"2026-05-27T10:03:57.234298Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"npj Computational Materials11, 9 (2025)","venue":null,"work_id":"1843aac0-8175-4aaa-92ea-9ff17214a775","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:7015a1991d45449b39d4319dfe15441d278cd07293b9aecf316950a3a483cc6a","observation_id":"2b4dad05-b55a-4329-90cd-9a0c3f350669","resolution":{"observed_at":"2026-05-27T10:03:57.237966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"https://github.com/ACEsuit/mace-foundations","venue":null,"work_id":"11e0a79f-d04d-4c67-8c90-324c8c93004b","year":null},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:84c436c19b40ac7de362aa77205558a6b20a85d7d49b618b46e460e57e88e489","observation_id":"3cd7ac42-8893-46fc-9ade-dfeedbcbc1b6","resolution":{"observed_at":"2026-05-27T10:03:57.153056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12771","last_updated":"2026-05-20T00:46:02Z","snapshot_observed_at":"2026-08-02T13:38:33.183923Z","submitted_at":"2024-10-16T17:48:34Z","title":"Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models","version":2},"cited_work":{"arxiv_id":"2410.12771","doi":"10.48550/arxiv.2410.12771","metadata_source":"pith","pith_arxiv_id":"2410.12771","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models","venue":"cond-mat.mtrl-sci","work_id":"22935efd-40fe-48d5-974f-f4c033759df0","year":2024},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"cited_paper":"/paper/2410.12771","citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:90296bec88e4da6397a4bd9b8eac5a615bf69431aacc5f1ef253b9f9b829c83c","observation_id":"b99209ba-aa02-4683-9971-55af1d8933bb","resolution":{"observed_at":"2026-05-16T23:42:26.544241Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-05-19T23:52:01.050592+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-19T23:52:01.050592+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"59e68743-5b76-473d-916f-a7972423bdbf","year":2017},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:507bade660ee86c7e4f8abece395aff34f677999780e53f5c5ef1dd5c506a16a","observation_id":"e87bf8e2-0c5e-49d3-9c00-ef8489ea22a7","resolution":{"observed_at":"2026-05-27T10:03:57.163434Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"PHON: A program to calculate phonons using the small displacement method","venue":null,"work_id":"9e3cedf2-243a-4bdd-b925-572e92ea927c","year":2009},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:7a398a15d83d2eb9226164a619e291cc24545ec6eb6f26f8698954536c1d8c90","observation_id":"69def5ea-abde-4073-acd2-0f99fc7d4c49","resolution":{"observed_at":"2026-05-27T10:03:57.157246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"First-principles Phonon Calculations with Phonopy and Phono3py.Journal of the Physical Society of Japan92, 012001 (2023)","venue":null,"work_id":"4b4ba59f-201d-48af-b004-1163267e5fdb","year":2023},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:659c1d462eaf0836be51e8f426f55fcebeb5ed83c151f15bf76aa64b0d5077dc","observation_id":"830dba47-012a-47b5-935e-dfacf35970f3","resolution":{"observed_at":"2026-05-27T10:03:57.167159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"0911.3186","last_updated":"2009-11-17T01:04:51Z","snapshot_observed_at":"2026-07-06T01:59:47.875682Z","submitted_at":"2009-11-17T01:04:51Z","title":"Crystal structure prediction using ab initio evolutionary techniques: principles and applications","version":1},"cited_work":{"arxiv_id":"0911.3186","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"0911.3186","snapshot_observed_at":"2026-07-04T17:16:04.003377Z","title":null,"venue":null,"work_id":"972565c6-ba6b-4da8-8c4a-fab38341f97b","year":2009},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"cited_paper":"/paper/0911.3186","citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:26c09929caef0eb286be0f5f72bcf4c05491e83e3a2e70bf805fca98b041b85c","observation_id":"431b730e-86ef-4cbe-8c56-bc3a18e71d97","resolution":{"observed_at":"2026-07-04T17:16:04.003377Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4e5eb3c8-b4a5-4c3d-8ffc-fbbad315b231","year":2008},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:d741ba9e2cf6d33ed2c75bd920419d88d3dd9833fa5f054f3a9b514b6955573b","observation_id":"01f0e234-1532-4db6-b309-92cccc0d47d4","resolution":{"observed_at":"2026-05-27T10:03:57.148564Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a5a70933-378c-462a-b450-b9c6f329e99b","year":2018},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:6719d139a645ba4e0279567196ea77bae83733b02fc25fed2c1158c7b46c7516","observation_id":"6c3d148c-3c8a-489a-a25b-ccac07cb2b02","resolution":{"observed_at":"2026-05-27T10:03:57.140953Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"S., Wei, L., Hu, M","venue":null,"work_id":"0d35eb1f-b4ea-4f7b-a81e-30f99aafaa24","year":2024},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:c30d81a997b08dbcaaa9f06ad1ce9b9e0b96f55dcfb70a2bd03589743f9321ff","observation_id":"17388aaa-ab61-497f-9576-9d56dde523df","resolution":{"observed_at":"2026-05-27T10:03:57.123277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"0e4903b9-83b3-41ac-9c24-becda77c406c","year":2024},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:dcfc44157f88460ce74bd6fe6b8b7957aec75efb61b652b4dcbbdf9166ea5afa","observation_id":"a9b40739-6f1f-4100-ba94-3af7690dfed6","resolution":{"observed_at":"2026-05-27T10:03:57.126906Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"& Ozgen, S","venue":null,"work_id":"a2f2d2bc-4020-4ada-ad9d-03746db9bb79","year":2013},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:ee75f0fc877e4f5b220a8efe5b7d337d625d26d421ff4dfd2a9a07791582cd0a","observation_id":"62bf10e5-b284-4467-8178-0b2c40b06047","resolution":{"observed_at":"2026-05-27T10:03:57.130151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"P.et al.Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis.Computational Materials Science68, 314–319 (2013)","venue":null,"work_id":"7835396e-1e61-45ad-90a1-631c71747269","year":2013},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:1e273b4eec742f183355817c4aa714778908f7fa24d94fbb865222900c3ae422","observation_id":"426bc314-6871-419b-8412-e9f27bbaeb6d","resolution":{"observed_at":"2026-05-27T10:03:57.116458Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"53a98f7a-ee76-4b7e-8fa2-b8b4083e07e2","year":2008},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:b6087591b882bb2163efa7f63e248d1bdc73d164bf08b645d434a1acdaad215f","observation_id":"41cb7a82-32c8-434a-bc2b-53f9edfbf4b6","resolution":{"observed_at":"2026-05-27T10:03:57.119828Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"S., Hart, G","venue":null,"work_id":"d7c4a8e2-8f78-4ad0-967b-a3fdfb81f9ce","year":2017},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:8855075ffecf3bb29f2b24215ed8f7ffdffeee0df65cf9699d8ecf56eb79308c","observation_id":"85bb60cf-a1cd-4d7d-9e10-252050ca85ec","resolution":{"observed_at":"2026-05-27T10:03:57.108029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2508.20875","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T07:34:21.391616Z","title":"arXiv preprint arXiv:2508.20875 , year=","venue":null,"work_id":"da991f36-684b-4133-a194-ce9db87364c9","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:44951fb8d16275463cec05579451f8863303c063d00f647016abd24e7952e551","observation_id":"9687dd42-81ac-4a07-98f6-33fcbf098579","resolution":{"observed_at":"2026-05-12T10:21:29.064160Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"LeMat-BulkUnique dataset (2023)","venue":null,"work_id":"9f4b1189-9a5f-42a6-a54b-09195608b84e","year":2023},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:b9ea718d1e4e681a7dc643b3a534afd3bb951edac6ec4823f82b4e7ddf0dbb44","observation_id":"4680f3f9-2cdd-4ad0-9a08-bd8f218df80f","resolution":{"observed_at":"2026-05-27T10:03:57.104339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"& Furthm¨ uller, J","venue":null,"work_id":"e26672d9-1d9c-42e4-8fa5-d1833f3ee85d","year":1996},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:64773b0ace50d8b0e98a682a53098bc94445ab57c463f7b03dcfb6d46435b34c","observation_id":"6b1ad22e-60c1-46cf-b85d-d110424164bc","resolution":{"observed_at":"2026-05-27T10:03:57.111821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"P., Burke, K","venue":null,"work_id":"42574c1c-9488-499f-98a8-85cfd98922bd","year":1996},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:b9c801d148e1c3471afdb6298f586fb26c1fc0dc684ebc25dc7d91fd0d0b7fdb","observation_id":"49cfb74f-0eed-4ad7-9985-7140b294612a","resolution":{"observed_at":"2026-05-27T10:03:57.137018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"& Joubert, D","venue":null,"work_id":"447bdde6-3d63-4f03-82f7-d5858562ebc9","year":1999},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:c7a13c40b8bf6c3eac566d941862a5ac2f9aea858d6ca65b266cccb578e2b022","observation_id":"53f42950-327d-4ad2-805d-674ad8e594f9","resolution":{"observed_at":"2026-05-27T10:03:57.144450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"[object Object] (2024)","venue":null,"work_id":"97772229-c6f2-4eb9-ae83-cb79740b69aa","year":2024},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:17d3d9a33fa2ad3989fe03af3e7a28ac2f21afee09d56327a2f9bfcef335c6e7","observation_id":"163b79ed-f62d-4932-a14d-e29fb9c1502a","resolution":{"observed_at":"2026-05-27T10:03:57.160379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"K.et al.Accelerated data-driven materials science with the Materials Project","venue":null,"work_id":"05cf277c-1fcc-445a-a393-aedd869fad1b","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:2a52a51165b629c4881b2e06b75410ba7fb64ea4655b37fc496ff1274032095a","observation_id":"4a135578-d3fd-4be8-aa6c-187d53b1bfb7","resolution":{"observed_at":"2026-05-27T10:03:57.227796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"mkk/0115583","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"population","venue":null,"work_id":"9e381730-7b6c-4008-90fc-4a4de42f5cb7","year":1955},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:a1da97971394cef112d0b66a1724aec1894ceb8afd1e8006547b178b44674169","observation_id":"022b3f79-885d-4235-a522-8ad3a84916f6","resolution":{"observed_at":"2026-05-12T10:21:29.024447Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","latest_version":1,"primary_category":"cond-mat.mtrl-sci","snapshot_observed_at":"2026-08-04T09:54:36.511610Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials"},"reference_resolution":{"displayed":66,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":15,"verified_fuzzy":33},"total_outbound_references":66},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 2 inbound Pith citation observations for arXiv:2604.27685."}