{"as_of":"2026-08-08T09:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a47db37f29211941d7ac08481295ff0bb03e73e97d4c8ff7a2709ff53f8b9c90","coverage":[{"denominator":17,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-23T08:11:33.996692Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.00819/citation-record","integrity":"/paper/2412.00819/integrity","json":"/paper/2412.00819/citation-record.json","paper":"/paper/2412.00819"},"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":"Kirklin, J","venue":null,"work_id":"d7b8258d-8cc5-4998-8915-7bcb6263e3ed","year":2015},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:41f28033f2d6cb10b98c7cb37d9bd291101e2b08984b413d67c94907fe933391","observation_id":"6b4e71b7-ee45-4dc2-a40b-78393d890e8e","resolution":{"observed_at":"2026-05-23T08:12:43.975925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"d71e5065-9f13-4b3d-b51f-81a9e091df42","year":2016},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:c5ddcba7d36d7ed19100c549c46021f0daf6a12fad3c7aa7c6edbd0cbb600a14","observation_id":"2b98e815-1d18-4c22-9263-a3e983513adb","resolution":{"observed_at":"2026-05-23T08:12:43.979645Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"9aca1bd5-d3b8-4ac5-a195-d3cd228495d2","year":2013},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:8a455645fa77b80257db5988264994b5def99633e122a9573461a45d87d749b1","observation_id":"14a01cc0-4297-44a0-a5b6-667895cadb3b","resolution":{"observed_at":"2026-05-23T08:12:43.971976Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2d5a862f-4e47-4499-bd16-b9712145ec86","year":2013},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:66810d15f238e427d337689b73db8141eae1cd1aa14fbf5d9b43602d753f5c68","observation_id":"2a80fbd0-2ec8-4bc6-8956-38faa2a79d04","resolution":{"observed_at":"2026-05-23T08:12:43.955797Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"7982906d-ab32-4bfc-a4d3-571fda00b8af","year":2018},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:a152d89aa5b271af07a3dfb6b4a6b239f2bcf06dd0f362c38fd53c9644845b1c","observation_id":"6c63d338-5f15-4ea0-a9c1-ec0ffa7b9c1c","resolution":{"observed_at":"2026-05-23T08:12:43.983511Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"0c9e7cc3-e633-4cf6-a757-07091f7f01ae","year":2018},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:ec949d18a82ac0a87406ef51d801975bfeeff103b7441e397b4b01d1e386fd1c","observation_id":"05c8a7bd-2940-403f-a92a-24d051f2bdff","resolution":{"observed_at":"2026-05-23T08:12:43.959774Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"158d1a8a-e1b6-4280-87f6-4979dcfd228f","year":2018},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:176636739e21b2f5cd450d200a47783161b654b7eb46c19f4df9bf9c16276d24","observation_id":"21edb538-db0b-4fc8-8f8f-7e1ffaf89778","resolution":{"observed_at":"2026-05-23T08:12:43.952009Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"f454eee7-4fc6-40a8-8fb9-4231e4fe2cbc","year":2020},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:0a489ae179248ecb96e01638bb4520b830eabe56b922758cddc99dd87228a158","observation_id":"e866fc82-e261-4cf1-8532-02097f984f56","resolution":{"observed_at":"2026-05-23T08:12:43.947838Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"4d1647c0-82a9-44bb-b8cc-00b91c5f8613","year":2018},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:bb188676242b7031c895ce0f074ab3363074456dd76143f1857fe9e8837f2ab6","observation_id":"5ab2d6a3-e753-48d6-809c-100e0bcedcac","resolution":{"observed_at":"2026-05-23T08:12:43.964157Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"ebf7e9a8-d171-4af4-8685-5ba6e2afe889","year":2017},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:97ff3d07d836adc43c046f17593de7467f9a0766053c0ffcca7cba05e17469ac","observation_id":"534c9814-44be-4500-874f-f99d06bb51bb","resolution":{"observed_at":"2026-05-23T08:12:43.968032Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"1bce53c6-fcd8-427b-b5b2-4a6f6cd64544","year":2018},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:0745ea4e07205f1f7fdacc0728476531c4e7b925972178c2ba515078235bd391","observation_id":"c280983c-56b6-4d67-9b60-f1ed6e59b321","resolution":{"observed_at":"2026-05-23T08:12:43.991733Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"130defe1-eb9b-4336-8646-4c78b5f7dbe8","year":2018},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:72dff88152d359874c441d11c0faff57844e2f8048e94f8fe6b3102bac6b5b47","observation_id":"77717c85-477e-4c62-99ce-6b6a97b11c01","resolution":{"observed_at":"2026-05-23T08:12:43.995623Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"Glorot, A","venue":null,"work_id":"e68c864d-01ab-458d-a5e2-8bef7172f1a8","year":2011},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:a0ca83527958311983a48647587d322ad747b4ac12a88a6934dd4ee809ee082a","observation_id":"e082a30c-a97a-4ec2-8c28-8fe3670c1208","resolution":{"observed_at":"2026-05-23T08:12:44.010180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"Kingma, J","venue":null,"work_id":"19c0f005-314e-4194-8b15-5c9ff0143997","year":2015},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:f6b9e9168d23d9b748584c6c7eb26a4ce5ab2897f30558db35c8cb2a675d0da1","observation_id":"d5f55aaa-56d3-408b-a9c6-769bb937cde9","resolution":{"observed_at":"2026-05-23T08:12:44.006625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"Duchi, E","venue":null,"work_id":"1eabfcb5-2185-42a4-a224-382223f4a1c4","year":2011},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:6abd0808f62497d45df53c6ad555bcae26db73260b107298ae578428b1e73d62","observation_id":"f112768c-e54a-4afd-9c5d-2a1feee8e0c7","resolution":{"observed_at":"2026-05-23T08:12:44.000017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"Tieleman, G","venue":null,"work_id":"5427557f-e1f7-408a-a4ff-fd4ddbca8fed","year":2012},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:b87dffb820060470d9841708734ece25536ad9bba6f3c2d4f2ce28115c8241ac","observation_id":"447d97c9-7248-408d-b680-05c3c35fcedf","resolution":{"observed_at":"2026-05-23T08:12:44.014187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"Van Rossum, The Python Library Reference, release 3.8.2, Python Software Foundation","venue":null,"work_id":"b2683ec5-28bd-46ca-9b68-e957c9257809","year":2020},"citing_paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-23T08:11:33.996692Z"},"links":{"citing_paper":"/paper/2412.00819"},"observation_digest":"sha256:f904ad0bfaa94449c44ecb5f5c48aae765e917b3d27d7e9a298b6f8b0a24c0e9","observation_id":"b32f7c89-0ab3-441b-9a3e-bd7ea9e7e631","resolution":{"observed_at":"2026-05-23T08:12:43.987751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.00819","last_updated":"2024-12-01T14:13:17Z","latest_version":1,"primary_category":"cond-mat.mtrl-sci","snapshot_observed_at":"2026-07-06T19:59:42.424537Z","submitted_at":"2024-12-01T14:13:17Z","title":"Formation Energy Prediction of Material Crystal Structures using Deep Learning"},"reference_resolution":{"displayed":17,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":0,"verified_fuzzy":6},"total_outbound_references":17},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2412.00819."}