{"as_of":"2026-08-17T18:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2fb9612000aa58f28034ddd6146ed9dc6371c30cb1ff3f68f1217ec75a832311","coverage":[{"denominator":299,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T20:41:09.228296Z","state":"measured"},{"denominator":102,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":102,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T15:58:19.744156Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-11T00:16:15.729268Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.09429","snapshot_observed_at":"2026-08-11T15:58:19.744156Z","title":"AI-driven inverse design of materials: Past, present and future","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10516","last_updated":"2025-03-19T03:10:46Z","snapshot_observed_at":"2026-08-16T04:05:19.909771Z","submitted_at":"2024-12-13T19:22:31Z","title":"CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties","version":4},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T15:58:19.744156Z"},"links":{"cited_paper":"/paper/2411.09429","citing_paper":"/paper/2412.10516"},"observation_digest":"sha256:6a951f6fb7d6245fd8ec152a4be72a9d56aadbf1ff2f7e6ee2c6f58524765195","observation_id":"5a867efe-2ca0-46a2-bdbc-d18095acaf0c","resolution":{"observed_at":"2026-08-11T15:58:19.744156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"cited_work":{"arxiv_id":"2411.09429","doi":"10.48550/arxiv.2411.09429","metadata_source":"pith","pith_arxiv_id":"2411.09429","snapshot_observed_at":"2026-08-11T00:16:15.729268Z","title":"AI-driven inverse design of materials: Past, present and future","venue":"cond-mat.mtrl-sci","work_id":"3774e77c-6e8a-4dc0-b083-9ea13222caf9","year":2024},"citing_paper":{"arxiv_id":"2501.04604","last_updated":"2025-03-08T18:15:50Z","snapshot_observed_at":"2026-08-14T06:00:45.020596Z","submitted_at":"2025-01-08T16:41:08Z","title":"Accelerated Discovery of Vanadium Oxide Compositions: A WGAN-VAE Framework for Materials Design","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T21:35:51.454035Z"},"links":{"cited_paper":"/paper/2411.09429","citing_paper":"/paper/2501.04604"},"observation_digest":"sha256:1ea0cfc481010f0068c98bf43b67b56c3b6ab645b391dad6cc464c8e58ed93ef","observation_id":"8f2a9c81-c01b-46ba-bdaa-21fcadd60fb4","resolution":{"observed_at":"2026-08-10T21:35:52.015989Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.09429/citation-record","integrity":"/paper/2411.09429/integrity","json":"/paper/2411.09429/citation-record.json","paper":"/paper/2411.09429"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.761328Z","title":"Inverse design in search of materials with target functionalities,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.761328Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:658c27763de8554daccfa37bb22d36cf8bfd231eea19e4b7e183d183995c8ec6","observation_id":"f4ff0fc3-32f2-4c35-b6b3-ea02bb411999","resolution":{"observed_at":"2026-08-12T20:41:08.761328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.766410Z","title":"Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.766410Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:4f717dd7c81e27234fc2b2af4db49b8d719d2d936008a07c0c41dbb9fcd0f366","observation_id":"7052b64b-270c-4f74-9a03-ffac29abba1f","resolution":{"observed_at":"2026-08-12T20:41:08.766410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.771284Z","title":"Inverse design of materials by machine learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.771284Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:a5677e068337cb7d473ae4208462f2d810bcf96a984a667a38c8c62be8c6b564","observation_id":"b67a5875-f2a5-4021-b3bc-d0000b2475b4","resolution":{"observed_at":"2026-08-12T20:41:08.771284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.19124","last_updated":"2024-09-27T20:10:19Z","snapshot_observed_at":"2026-08-16T13:14:38.585277Z","submitted_at":"2024-09-27T20:10:19Z","title":"Generative deep learning for the inverse design of materials","version":1},"cited_work":{"arxiv_id":"2409.19124","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.19124","snapshot_observed_at":"2026-08-12T20:41:11.154494Z","title":"Generative deep learning for the inverse design of materials","venue":"cond-mat.mtrl-sci","work_id":"9238ef6a-b1fa-4160-8edb-3ff5ac44e902","year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.778138Z"},"links":{"cited_paper":"/paper/2409.19124","citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:bcde1717484230626194cb3d1bf35b35bff55d9f31a19309e6e1f9b1a70a161b","observation_id":"1b529710-e555-4142-9608-4486d15a4c0e","resolution":{"observed_at":"2026-08-12T20:41:11.159178Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.783782Z","title":"Physics-informed machine learning methods for inverse design of multi-phase materials with targeted me- chanical properties,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.783782Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:9410c0732248c1b0908736625f283ce1b30fffd64770277f5e880a775e7c89a6","observation_id":"ebee87aa-fec3-44a2-926c-efb1a0fb606c","resolution":{"observed_at":"2026-08-12T20:41:08.783782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.788410Z","title":"Generative models for inverse design of inorganic solid materials,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.788410Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:45a7f5c0aba80d4c20c0a0d4b0c89635b487760a08981777c214035658a98a64","observation_id":"5c0f7485-5e0d-473d-975f-74f58d5df29d","resolution":{"observed_at":"2026-08-12T20:41:08.788410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.793538Z","title":"Inverse design of 3d cellular materials with physics-guided machine learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.793538Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:fb5673e47f70fccf0b2910a4dbca25c12a2a9918cfcff2b1d89881cb0bdf21f9","observation_id":"55a0c11d-37d4-4e84-b1bb-f9efbea0514d","resolution":{"observed_at":"2026-08-12T20:41:08.793538Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.799409Z","title":"Further experiments with liquid helium,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.799409Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:ffa0ef87a70d5b9a6854b4e8f01996e2c88e83cfbeab9c11e3068dd93926e8c9","observation_id":"95476a03-fbd1-4771-ba41-86b8df57d45e","resolution":{"observed_at":"2026-08-12T20:41:08.799409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.806358Z","title":"Superconductivity at 39 k in magnesium diboride,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.806358Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:591bf7f58f5a0f15a7803f503079b1d73ef72df61acfe54c3d56182b11e7f581","observation_id":"de2b3652-2e4a-48d9-bdb4-313a13cb0f12","resolution":{"observed_at":"2026-08-12T20:41:08.806358Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.810519Z","title":"Inverse design of porous ma- terials using artificial neural networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.810519Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:3f831800e91c317e67ca1bf4f89be93e1b37a2b0d9e98d63c7fbc0be1743dfb0","observation_id":"e9aa7bb9-8d68-423e-9f5f-de59cca0b1b4","resolution":{"observed_at":"2026-08-12T20:41:08.810519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.815420Z","title":"The dirac equation and the prediction of antimatter,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.815420Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:531ec52afb8de377088c9a24dc3e265543599a801d7220daa216d70bea679e05","observation_id":"f15f8de6-54d5-4869-9ed7-973115ab18ef","resolution":{"observed_at":"2026-08-12T20:41:08.815420Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.819701Z","title":"The apparent existence of easily de- flectable positives,","venue":null,"work_id":null,"year":1967},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.819701Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:91216dd36adf4a1dad7bacbe9796346a44a4f18dc664f4fd03760890a569fda2","observation_id":"c1607adf-3a24-4468-8c3a-7081f73fb8a6","resolution":{"observed_at":"2026-08-12T20:41:08.819701Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.823879Z","title":"Theory of superconductivity,","venue":null,"work_id":null,"year":1957},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.823879Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:f16400110b988b7c000ab5df803973156fad810e3b9f909448bb9fe90fa1fe53","observation_id":"97952884-d6ae-47d2-8b73-b30624a8bd87","resolution":{"observed_at":"2026-08-12T20:41:08.823879Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.828221Z","title":"Theory of the meissner effect in superconduc- tors,","venue":null,"work_id":null,"year":1955},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.828221Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:fe9042b7126c322c010084bf5a87aab960398792b74c04a4e7efe270fcf36731","observation_id":"df55483f-1236-4448-8240-ea7adb3bba6b","resolution":{"observed_at":"2026-08-12T20:41:08.828221Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.832537Z","title":"Bound electron pairs in a degenerate fermi gas,","venue":null,"work_id":null,"year":1956},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.832537Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:63ea48d37b1d00354f83b69ad2de09ad4bb0c580ef6a7bad257fc034677db5bc","observation_id":"9c49ad4b-a0cc-4adc-8457-8e352561c160","resolution":{"observed_at":"2026-08-12T20:41:08.832537Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.836825Z","title":"Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set,","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.836825Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:d04e1fd48f95213e706558105f24a024b16f41efd49f190eb8466206606e3ab9","observation_id":"b16492ff-24ea-4f7d-a007-eed8876e5374","resolution":{"observed_at":"2026-08-12T20:41:08.836825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.842445Z","title":"Generalized gradient approximation made simple,","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.842445Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:d7c5a575cb6da3db487b56fb9b527d99d4dec3092144b07e9958b2526be96396","observation_id":"b4a62a69-a847-4163-8e4a-0bc353d3dd56","resolution":{"observed_at":"2026-08-12T20:41:08.842445Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.846737Z","title":"Projector augmented-wave method,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.846737Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:ac830d4b7d32d77bfbe3360fbc92f988891f7f706f0df38cbb13da65068eee2a","observation_id":"efed26ad-bf0a-4716-8701-1be97d79e7a1","resolution":{"observed_at":"2026-08-12T20:41:08.846737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.855138Z","title":"Electric field effect in atomically thin carbon films,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.855138Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:54144fb03ea6b5c12ec4b45bf50b4a98782c7229e6a3f30e3b673a83c6e3bb73","observation_id":"c540f933-c53f-408b-8418-8dd8dfa71e13","resolution":{"observed_at":"2026-08-12T20:41:08.855138Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.859475Z","title":"Theoretical models incorporating electron correlation,","venue":null,"work_id":null,"year":1976},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.859475Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:42407dff3c1b9c809f82796c7c46b9fdd8701e31fb0300135ccb8c83631d0e5f","observation_id":"436a8153-88f3-4ae1-8219-fcd50ad37da6","resolution":{"observed_at":"2026-08-12T20:41:08.859475Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.863497Z","title":"Has generative artificial in- telligence solved inverse materials design?","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.863497Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:4882506e14b55d79af0afce900f423398c0ff760f7a2459fbb8d085de142e1f8","observation_id":"ac0cefef-3436-4558-ab38-f6b6287d7e45","resolution":{"observed_at":"2026-08-12T20:41:08.863497Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.867615Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.867615Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:235347064debcf328fb233cba69e416fc5015da6420b2c87093df68059338784","observation_id":"3e9f2abf-bd19-4d88-adbb-7668875dc1b5","resolution":{"observed_at":"2026-08-12T20:41:08.867615Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.06197","last_updated":"2022-03-14T13:36:09Z","snapshot_observed_at":"2026-08-16T17:49:53.703761Z","submitted_at":"2021-10-12T17:49:49Z","title":"Crystal Diffusion Variational Autoencoder for Periodic Material Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.06197","snapshot_observed_at":"2026-08-12T20:41:08.871545Z","title":"Crystal diffusion variational autoencoder for periodic material generation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.871545Z"},"links":{"cited_paper":"/paper/2110.06197","citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:d5998e398673d441802506295e3fa735492c7c9a1ba3aa578220053f2037e677","observation_id":"caef050f-ed6d-4d22-8d63-8aaac7f303a7","resolution":{"observed_at":"2026-08-12T20:41:08.871545Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.876062Z","title":"Crystal structure prediction by joint equivariant diffusion,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.876062Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:72e04fc5e83e49f6eecdc4d98e0d9a0b5874643c96d7269c38e0340e8954f265","observation_id":"d0cdda63-a803-4254-813f-0b66e8292ab0","resolution":{"observed_at":"2026-08-12T20:41:08.876062Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.07608","last_updated":"2024-08-14T15:12:05Z","snapshot_observed_at":"2026-08-16T13:26:27.988637Z","submitted_at":"2024-08-14T15:12:05Z","title":"MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.07608","snapshot_observed_at":"2026-08-12T20:41:08.880220Z","title":"Mattergpt: A generative trans- former for multi-property inverse design of solid-state ma- terials,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.880220Z"},"links":{"cited_paper":"/paper/2408.07608","citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:2dddecc8d6e7b39135c72151772ad8334de5331fce6494b56e292f55d406cdf9","observation_id":"5d7be0a0-0420-423b-9a77-0fb1a70b1a90","resolution":{"observed_at":"2026-08-12T20:41:08.880220Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.884610Z","title":"Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.884610Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:8b4081cf90524f6313d566daa93b1e68a9c179d24607423fb94ed4370a6943c8","observation_id":"1f67ea1b-624b-48e5-8f26-15c04ccb9363","resolution":{"observed_at":"2026-08-12T20:41:08.884610Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.888438Z","title":"Denoising diffusion probabilistic models for generative alloy design,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.888438Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:b9d6886678565e0e55c36b80d3e80f9e63892c3866f24d500a24f2dcaa654d2b","observation_id":"a2549705-f67f-4833-968a-e7c21b599ce2","resolution":{"observed_at":"2026-08-12T20:41:08.888438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.892346Z","title":"Microstructure reconstruction of 2d/3d random materials via diffusion-based deep generative models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.892346Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:ac7d78a6ca7c4b16a2d3267347415c92424847d99989b3e850256028e5015c9c","observation_id":"b52994eb-b493-443c-955d-09fbd114851b","resolution":{"observed_at":"2026-08-12T20:41:08.892346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.03963","last_updated":"2024-10-04T23:03:57Z","snapshot_observed_at":"2026-08-16T13:12:25.222423Z","submitted_at":"2024-10-04T23:03:57Z","title":"dZiner: Rational Inverse Design of Materials with AI Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.03963","snapshot_observed_at":"2026-08-12T20:41:08.900615Z","title":"dziner: Rational inverse design of materials with ai agents,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.900615Z"},"links":{"cited_paper":"/paper/2410.03963","citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:14367daefa9cfdd3b6990cb90a127b5bc3789e8f2ffb5e2f98768b6415a57fa7","observation_id":"a923f7ce-4bad-4976-829f-f25afed75d4a","resolution":{"observed_at":"2026-08-12T20:41:08.900615Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.905222Z","title":"Scaling deep learning for materials discovery,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.905222Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:ae2b4dfd8b0843dfda0a4509fcb07e37a39741f77c6598a4d8e6285c686c2c4c","observation_id":"f138d473-3a9d-4f2d-a663-0c5baf9e87f6","resolution":{"observed_at":"2026-08-12T20:41:08.905222Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12771","last_updated":"2026-05-20T00:46:02Z","snapshot_observed_at":"2026-08-15T03:47:51.196617Z","submitted_at":"2024-10-16T17:48:34Z","title":"Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.12771","snapshot_observed_at":"2026-08-12T20:41:08.909654Z","title":"Open materials 2024 (omat24) inorganic materi- als dataset and models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.909654Z"},"links":{"cited_paper":"/paper/2410.12771","citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:e03940b35b0244e7fd903a9a8986f716e1ee3c273ac246f3e457979395543565","observation_id":"92faac5e-fcb3-426c-8e0f-f5c5a7d63b50","resolution":{"observed_at":"2026-08-12T20:41:08.909654Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.914181Z","title":"Inverse design of photonic and phononic topological insulators: a review,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.914181Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:b3c3c80408d406ea571f9b5d9a4740fca95c6beec9c53267df11ad61b4a7706d","observation_id":"7c2026fd-8dc2-4caa-9c36-89de98b273b1","resolution":{"observed_at":"2026-08-12T20:41:08.914181Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.918397Z","title":"Machine learning design for high- entropy alloys: models and algorithms,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.918397Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:cb63f63c7a58c2450204277717c9c7972a2cb76a5697cdbc7dec0f87f4b14024","observation_id":"62d6747b-07bc-41dd-adc2-7da78673301d","resolution":{"observed_at":"2026-08-12T20:41:08.918397Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.00485","last_updated":"2025-05-30T06:56:59Z","snapshot_observed_at":"2026-08-16T14:13:46.912391Z","submitted_at":"2024-03-01T12:13:04Z","title":"A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.00485","snapshot_observed_at":"2026-08-12T20:41:08.922467Z","title":"A survey of geometric graph neural networks: Data structures, models and applications,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.922467Z"},"links":{"cited_paper":"/paper/2403.00485","citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:d94d87c6ad59e8cfc614074de4bfcbe0efa4c48f2f051472fcc269d1b10eca1d","observation_id":"e311df05-75b1-41a6-8f9b-a08ad02fe52c","resolution":{"observed_at":"2026-08-12T20:41:08.922467Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.927499Z","title":"Graph neural networks for materials science and chemistry,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.927499Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:3cd462c23d40be13e5c1a09530ecff3bff69cd5da3eae662f530c420ea4c4467","observation_id":"f6805eb3-0c43-45f4-b354-c97287c11e6c","resolution":{"observed_at":"2026-08-12T20:41:08.927499Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.932499Z","title":"Novel technologies and configurations of superconducting magnets for mri,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.932499Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:dcfcd092b2e5cfdc0370106665e674dc0580a896988833d6597d15f8651995e2","observation_id":"25e80b42-76c9-4973-80a8-450cdfa43a12","resolution":{"observed_at":"2026-08-12T20:41:08.932499Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.939623Z","title":"High temperature su- perconductors for fusion magnets,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.939623Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:89ab79d5a57b218709c8741f1c0963c27043f38f2a5ee2302c2b497d149ab96d","observation_id":"4979d788-202c-49c9-844f-2c4556bbabf5","resolution":{"observed_at":"2026-08-12T20:41:08.939623Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.947316Z","title":"Superconducting qubit to optical photon transduction,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.947316Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:6cc1342e0be5f2d00f7656c47ea9b9080a1a8daa589c4e046e8705dd6fb62fea","observation_id":"5c5c3fd3-f289-4994-8bf8-e997006c4eaf","resolution":{"observed_at":"2026-08-12T20:41:08.947316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.954400Z","title":"Building logical qubits in a superconducting quantum computing system,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.954400Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:94d918b475918e05e83154e174697882c1be6af3573554aac510e3e155b858da","observation_id":"6aede6d9-c172-4f1b-b74f-5009220b8f66","resolution":{"observed_at":"2026-08-12T20:41:08.954400Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.958787Z","title":"Quantum sensing,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.958787Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:19501b9c385a05bb4ca46d8edb2d1ea2ed6fe0d8656e0a019132a492898d04a3","observation_id":"b220aa6a-5e50-404b-a4e5-0018e50e85f8","resolution":{"observed_at":"2026-08-12T20:41:08.958787Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.972581Z","title":"The resistance of pure mercury at helium temperatures,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.972581Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:5996fe1a9df31c915045458a9ebcd04d9d5e6b297fcf6ba6aafdd35d46d7ed02","observation_id":"cfa72901-c6cd-4f62-8214-4139b0bab0bf","resolution":{"observed_at":"2026-08-12T20:41:08.972581Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.977256Z","title":"Ein neuer effekt bei eintritt der supraleitfähigkeit,","venue":null,"work_id":null,"year":1933},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.977256Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:74e8ba07211db3d93f570bc307b7f785fd9739140055890e615621e9e199367f","observation_id":"55f27425-e39f-4186-8eee-d228e513b6a2","resolution":{"observed_at":"2026-08-12T20:41:08.977256Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.981473Z","title":"Superconductivity in nb–ge films above 22 k,","venue":null,"work_id":null,"year":1973},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.981473Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:01b76fcbcea4d45bde70d26e50aa8f9e3ebd0bbf5832e10854ee6aa5f2697339","observation_id":"a363f9b3-0b6f-412d-afb8-83ca9900b01d","resolution":{"observed_at":"2026-08-12T20:41:08.981473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.985879Z","title":"Possible high t c super- conductivity in the ba- la- cu- o system,","venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.985879Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:2d42af7554d809dc77b5d2e05a7c75c61d4f258cfe0c87a4115fc7f336bf2a4a","observation_id":"35bf9761-54cb-48bd-8b37-5fecba0b65c9","resolution":{"observed_at":"2026-08-12T20:41:08.985879Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.989820Z","title":"Superconductivity at 93 k in a new mixed-phase y-ba-cu-o compound system at ambient pressure,","venue":null,"work_id":null,"year":1987},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.989820Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:bb22e855be1884b99f5aa86587629fefceede7945fac8a7810b6aa496d8d3392","observation_id":"30612d59-7297-4f32-948b-2df0a7212b21","resolution":{"observed_at":"2026-08-12T20:41:08.989820Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:08.994432Z","title":"Superconductivity at 55 k in iron-based f-doped layered quaternary compound sm[o 1−xfx] feas,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.994432Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:f5cc52bc71a046d4e7e1abf3f50db1abc7d233e27a23ca8a6272f139cb6011cc","observation_id":"3095d327-6ec6-48ab-8152-298baea2ab6c","resolution":{"observed_at":"2026-08-12T20:41:08.994432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1103/physrevlett.130.256002","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:10.563482Z","title":"Record high 36 k transition temperature to the superconducting state of elemental scandium at a pressure of 260 gpa,","venue":null,"work_id":"42bb053a-3edf-4b07-bc9c-fcab70a6fadd","year":2023},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:08.998383Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:c6ccc5065ac0a4f2e48b47674201682fac1cfbe33ce0a5a996d39306b5dd71a2","observation_id":"f2492b5a-f5f0-42e7-ac35-4abcd064a75d","resolution":{"observed_at":"2026-08-12T20:41:10.570055Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.002781Z","title":"Signatures of superconductivity near 80 K in a nickelate under high pressure,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.002781Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:eeb2616f8a375fe68f4d1c71999335927c16eca4e9e6f900e834449614f17ddb","observation_id":"c79a17f5-31d3-4bec-92b0-8026cefd58f7","resolution":{"observed_at":"2026-08-12T20:41:09.002781Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.006958Z","title":"Conventional superconductivity at 203 kelvin at high pressures in the sulfur hydride system,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.006958Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:19cac7b20ba6d66da6ff3d0abba0bd748ae058543c98400c66d3fd3335f293fb","observation_id":"cb3ced3f-554d-4485-ba6a-c7833ef13029","resolution":{"observed_at":"2026-08-12T20:41:09.006958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.011789Z","title":"Machine learning modeling of superconducting critical temperature,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.011789Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:3aeaf271476816a699d2ecb1f088ad97516645475b76b2a2d1411caf79443658","observation_id":"83976ba3-2e9d-49d2-8027-162b865f59d9","resolution":{"observed_at":"2026-08-12T20:41:09.011789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.016013Z","title":"A general-purpose machine learning framework for predict- ing properties of inorganic materials,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.016013Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:c04737a62a7af7b0b738b1c59f1424506a874a36fcc85d7a5fca59e374a78ff0","observation_id":"bdf4cd09-281f-49e9-babf-37382473c594","resolution":{"observed_at":"2026-08-12T20:41:09.016013Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.07721","last_updated":"2024-09-12T03:02:59Z","snapshot_observed_at":"2026-08-16T13:19:16.659792Z","submitted_at":"2024-09-12T03:02:59Z","title":"A deep learning approach to search for superconductors from electronic bands","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.07721","snapshot_observed_at":"2026-08-12T20:41:09.020579Z","title":"A deep learning approach to search for superconductors from electronic bands,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.020579Z"},"links":{"cited_paper":"/paper/2409.07721","citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:9ff4a858c69557249abcb2df260abcd253bcc503cbf20874e6a228431b008688","observation_id":"23c45b7a-a92d-428c-8870-403cee02129e","resolution":{"observed_at":"2026-08-12T20:41:09.020579Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.09419","last_updated":"2024-09-14T11:43:55Z","snapshot_observed_at":"2026-08-16T13:18:34.249666Z","submitted_at":"2024-09-14T11:43:55Z","title":"Superband: an Electronic-band and Fermi surface structure database of superconductors","version":1},"cited_work":{"arxiv_id":"2409.09419","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.09419","snapshot_observed_at":"2026-08-12T20:41:10.967950Z","title":"Superband: an Electronic-band and Fermi surface structure database of superconductors","venue":"cond-mat.supr-con","work_id":"c5204ea3-a7d9-4f70-822e-06957ff97389","year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.025249Z"},"links":{"cited_paper":"/paper/2409.09419","citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:d2246a2fbd2bc9b016f58c1993401355b54da67e98f73ef3a4c5617919dab35f","observation_id":"ae6eee45-be1b-45cc-a27d-45420604d91b","resolution":{"observed_at":"2026-08-12T20:41:10.972796Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.030023Z","title":"The graph neural network model,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.030023Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:51341399fb10e74cfbb15d82570e321edf7b3756c9c38f3a843a2761411679b1","observation_id":"d96157a6-225a-491d-9ad7-a2d87c3c7c16","resolution":{"observed_at":"2026-08-12T20:41:09.030023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.035269Z","title":"A comprehensive survey on graph neural networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.035269Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:f38aeeef587b2a20e32f20ab159f229ef9a52cd286bde969a47dae74c48d7b36","observation_id":"e0f72bdc-8c1c-4a1c-8b25-a2f656301f36","resolution":{"observed_at":"2026-08-12T20:41:09.035269Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-17T10:49:36.026134Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-12T20:41:09.039272Z","title":"Semi-supervised classifica- tion with graph convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.039272Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:0f91eef9fcc4a6cfbc5af58409258d163fd5d728171fb6bebf6bf8b84054c3b0","observation_id":"38843e50-7e9b-4df2-bbc9-57ec71e5f9dc","resolution":{"observed_at":"2026-08-12T20:41:09.039272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.043569Z","title":"Inductive rep- resentation learning on large graphs,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.043569Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:79a612d3aa7c00818117ddb0883f2aa6fec0ccc24e8a14cf38fd357cf9f165d1","observation_id":"c13fcfa5-d487-4cc8-93da-4f9cc13acbdb","resolution":{"observed_at":"2026-08-12T20:41:09.043569Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-08-13T22:35:40.714745Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-12T20:41:09.047594Z","title":"Graph attention networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.047594Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:90bfb5f07b508d5ecadf967bbacbbe38da3333bdf84f34c99c22c24e3bad0706","observation_id":"017043aa-5bc8-4474-b3ba-8d54fdc7c740","resolution":{"observed_at":"2026-08-12T20:41:09.047594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.051810Z","title":"Convo- lutional neural networks on graphs with fast localized spec- tral filtering,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.051810Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:1848f7b9e78f0a7556cad96b2a6ae09f4edbd67c131b816825bd37a271c0471d","observation_id":"15fbf205-a8ae-4644-b77b-719730b8bc47","resolution":{"observed_at":"2026-08-12T20:41:09.051810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.055933Z","title":"Designing high-tc super- conductors with bcs-inspired screening, density functional theory, and deep-learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.055933Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:2e8f63a6b53b901857ad7dd6a1d26452d0eb52be8fcebb5b4c2808e145fd3ea5","observation_id":"75ca29ec-d6fc-4b29-80fe-45f2b5c4ab57","resolution":{"observed_at":"2026-08-12T20:41:09.055933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.060368Z","title":"Atomistic line graph neural network for improved materials property predictions,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.060368Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:5b2907d95164fd5bafafc835b5d9d424be1f2dd225a829ac443e3d65da9f3211","observation_id":"abf632e8-da47-4f59-8355-0ff43e9a5885","resolution":{"observed_at":"2026-08-12T20:41:09.060368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.064600Z","title":"Data-driven design of high pressure hydride superconductors using dft and deep learn- ing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.064600Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:673eb3944014366f941d456861f55461a7e709b8b14c5f76b9f99a4c7f46a47c","observation_id":"973d56c7-51f8-4393-be3b-f502641f685a","resolution":{"observed_at":"2026-08-12T20:41:09.064600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.068872Z","title":"Machine learning model for predicting the critical transition temperature of hydride superconductors,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.068872Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:2ef260d54f8e2abd591327b513cdf6f50508d596c5f4b5d58962c97946ef7ca8","observation_id":"bbace50e-9524-4e12-a65a-0aec3cc415fa","resolution":{"observed_at":"2026-08-12T20:41:09.068872Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.073119Z","title":"Machine learning accelerated discovery of superconduct- ing two-dimensional janus transition metal sulfhydrates,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.073119Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:30657657eb493da3b82899824a99cea8a5e80a16a9f2ff976795b99a0cf67039","observation_id":"f5a979d4-14e7-4fb0-a8b0-ae8f2f942e58","resolution":{"observed_at":"2026-08-12T20:41:09.073119Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.077495Z","title":"Searching materials space for hydride superconductors at ambient pressure,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.077495Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:6e99b03a306eed893e7bd2e690641d127fdcd5fbc3542c06bec7a732610ac89f","observation_id":"db864cd6-c3d0-4ff0-a71a-d5ce06c51fed","resolution":{"observed_at":"2026-08-12T20:41:09.077495Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.081739Z","title":"3dsc- a dataset of superconductors including crystal structures,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.081739Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:ee90f54ddfc45576cbb827ee5a886953e1f5153f0c232b005296a5868bb6b389","observation_id":"e4ae2216-d766-4c4b-8cee-8a4bb62074d2","resolution":{"observed_at":"2026-08-12T20:41:09.081739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.086634Z","title":"High-resolution image synthesis with latent diffusion models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.086634Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:c40d1ed944196ed03ac2516f383ee45bc967dca78332e4a623553104cfbab1fb","observation_id":"e8b6e8c6-fd85-4eaa-8120-101c1bfe6d81","resolution":{"observed_at":"2026-08-12T20:41:09.086634Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.090817Z","title":"Denoising diffusion proba- bilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.090817Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:a0c6443b160f56c7699262f87d044e1c3a40e2109b8ceb05a7db921879056237","observation_id":"a67d0c0b-d660-4663-ae04-0bcc651ed6fb","resolution":{"observed_at":"2026-08-12T20:41:09.090817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.094840Z","title":"Generative modeling by estimating gradients of the data distribution,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.094840Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:99fd9cd55e5e5b653c4291adaae03b384bc74923b1281f6b5c70d062c38502a2","observation_id":"c1df12e9-f601-41de-a183-183dc8625c3e","resolution":{"observed_at":"2026-08-12T20:41:09.094840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.099520Z","title":"Accurate structure prediction of biomolecular interactions with alphafold 3,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.099520Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:7abc55d5563f7c5be41e34441d2ab704b496dec86a0a23b54220452566b9fd71","observation_id":"3008dc76-5406-4e29-9e8c-360955e2847d","resolution":{"observed_at":"2026-08-12T20:41:09.099520Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.08065","last_updated":"2025-05-13T08:22:00Z","snapshot_observed_at":"2026-08-17T15:16:26.626754Z","submitted_at":"2024-09-12T14:16:56Z","title":"InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.08065","snapshot_observed_at":"2026-08-12T20:41:09.103796Z","title":"Ai-accelerated discovery of high critical temperature superconductors,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.103796Z"},"links":{"cited_paper":"/paper/2409.08065","citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:78c9282ae4f59a3b0a845ba929e94faac7f298a2776206f67d1de555d58d27ac","observation_id":"d076d199-be55-4694-9405-946e6dfde354","resolution":{"observed_at":"2026-08-12T20:41:09.103796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.108174Z","title":"Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.108174Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:39ac1b90f8251fd60b41dbcfb5efe13281b8af5a59bfb829b2d3a4eaba204a1b","observation_id":"72fbcfa0-2c89-46d9-9744-5cc1b30bdbfc","resolution":{"observed_at":"2026-08-12T20:41:09.108174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.04418","last_updated":"2025-05-13T08:00:39Z","snapshot_observed_at":"2026-08-16T14:45:06.589670Z","submitted_at":"2023-11-08T01:06:48Z","title":"AI-accelerated Discovery of Altermagnetic Materials","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.04418","snapshot_observed_at":"2026-08-12T20:41:09.112660Z","title":"Ai-accelerated discovery of altermagnetic materials,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.112660Z"},"links":{"cited_paper":"/paper/2311.04418","citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:e3d2e48c245961db3775a12a0e7a1b4e359343046c385a5c69708b0d3fe3d9c2","observation_id":"b2a74c30-446a-4f15-b7d6-a4894bd6d209","resolution":{"observed_at":"2026-08-12T20:41:09.112660Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.116805Z","title":"Graph networks as a universal machine learning framework for molecules and crystals,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.116805Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:f4e96ec9305aea1a74b9f65fec89d484699363bff94711777caf09700ebe0ab2","observation_id":"8031fabc-2a6e-4f73-9f2b-2811d9e01200","resolution":{"observed_at":"2026-08-12T20:41:09.116805Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1103/physrevb.108.024512","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:10.522411Z","title":"Machine learning guided discovery of superconducting calcium borocarbides,","venue":null,"work_id":"c263b81b-1c19-4a87-ab88-a7e8dd122b2f","year":2023},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.121133Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:77d40181fa526e0410b15a016716748e16bda2f3a181f76062dab63c40251211","observation_id":"7d3006d1-5169-4bc7-8472-4c667782732b","resolution":{"observed_at":"2026-08-12T20:41:10.527861Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.15492","last_updated":"2024-08-16T06:29:55Z","snapshot_observed_at":"2026-08-16T14:31:52.837574Z","submitted_at":"2023-12-24T14:35:56Z","title":"DPA-2: a large atomic model as a multi-task learner","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.15492","snapshot_observed_at":"2026-08-12T20:41:09.125201Z","title":"Dpa-2: Towards a universal large atomic model for molecular and material simulation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.125201Z"},"links":{"cited_paper":"/paper/2312.15492","citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:ca0fb40f5e538c6e8d438b6afc5f18986757e0a52fcb9400e5de4ff6a87e62eb","observation_id":"62ee4980-a643-4856-abfa-35b86ec55ec0","resolution":{"observed_at":"2026-08-12T20:41:09.125201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.129622Z","title":"Inverse design of next-generation superconductors using data-driven deep generative models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.129622Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:7d82c661e035cf8cf225a53e94b41d91dd3993331f111c634e5ec82a665dcbd1","observation_id":"33849d40-1670-4ae7-8cbe-93a629cf3d9a","resolution":{"observed_at":"2026-08-12T20:41:09.129622Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.134124Z","title":"Virtual node graph neural network for full phonon prediction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.134124Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:b1fb8c1090968d842b59be265002f13cb9dab85c6b59e1ab224d1611a5a7669a","observation_id":"18579762-3723-469f-a556-f6d64804a0a1","resolution":{"observed_at":"2026-08-12T20:41:09.134124Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.138190Z","title":"Accelerating the calculation of electron–phonon coupling strength with machine learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.138190Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:deea5ef1851484560c3b8b71478ae926775a60800447917d45017f552ab676a6","observation_id":"c20bf300-c1f1-4691-9b19-957dac6d58ec","resolution":{"observed_at":"2026-08-12T20:41:09.138190Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.142211Z","title":"Scaling of transition temperature and cuo2 plane buckling in a high-temperature superconductor,","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.142211Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:02be12f868f900d12f27905e3f1b1951d6bcd9a1c4af9303afad4a5b2c1b2c5e","observation_id":"14e46cb4-edb1-4433-af53-b3bee763d3f0","resolution":{"observed_at":"2026-08-12T20:41:09.142211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.146031Z","title":"Relationship between crystal structure and superconduc- tivity in iron-based superconductors,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.146031Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:3eb854d95304b7ed8a6092e9e01a6a4623588f407f49297d1c391982b2408bcd","observation_id":"b102d9f5-fcf5-429c-adf2-18de1465a3e0","resolution":{"observed_at":"2026-08-12T20:41:09.146031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.150061Z","title":"Anion height dependence of tc for the fe-based supercon- ductor,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.150061Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:0609afcb6d385131798e7cc486e3ce73590bc1fa530eea14717cb7ce78e07759","observation_id":"57b7f1de-9122-4788-ad6d-b1f0bbfbc04c","resolution":{"observed_at":"2026-08-12T20:41:09.150061Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.154126Z","title":"Influence of apical oxygen on the extent of in-plane exchange interaction in cuprate superconductors,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.154126Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:317eb3bf455e9f8ad179c38ee7f8c8ec62dabf5ded5bfd90026c873caf9a6f6f","observation_id":"2b62fb54-8088-428a-9e6e-c75824397a8c","resolution":{"observed_at":"2026-08-12T20:41:09.154126Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.158152Z","title":"Bond sensitive graph neural networks for predicting high temperature superconductors,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.158152Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:bfd96c0635ccae29d2908c91b43c780822e5129da273587737254f1c7ca3071b","observation_id":"948b605e-c919-4848-bfc1-4a6755843bfa","resolution":{"observed_at":"2026-08-12T20:41:09.158152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.162091Z","title":"Closed-loop superconducting materials discovery,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.162091Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:d436404b880a1eb4ba904515a978920b8237756e585333f563fb831074c568a6","observation_id":"6940d9ef-ea9e-41b9-b5be-a00f04f18db0","resolution":{"observed_at":"2026-08-12T20:41:09.162091Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.171254Z","title":null,"venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.171254Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:8c09ebdfccc7959710edc0555181b25faeb6df7c40604b404ed13dd6d01f8da2","observation_id":"f6cab444-db99-40e0-a9be-39c10cfacfa5","resolution":{"observed_at":"2026-08-12T20:41:09.171254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.175004Z","title":"Goldman, Handbook of modern ferromagnetic materials","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.175004Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:3c06282747152a8d4f0db974716e0e08e6f44c1e71202f040ee18fe62d8ddcb2","observation_id":"3d32a4bb-03e4-497e-8907-c3123d85fefa","resolution":{"observed_at":"2026-08-12T20:41:09.175004Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.178858Z","title":"Anti- ferromagnetic spintronics,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.178858Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:0734dc25c15289cbed92ddbb8695368acbfccc8877daf1453ed68fc051176fcf","observation_id":"ecbcf7d3-8884-462b-ab12-0028ac16e094","resolution":{"observed_at":"2026-08-12T20:41:09.178858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.183083Z","title":"Emerging research landscape of altermagnetism,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.183083Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:d2bb4c8a713d1b1b1eb19d294ee449c683f2bcd73cc8facf96a402cade735117","observation_id":"500ce9c5-9281-46de-81b0-0112266becb7","resolution":{"observed_at":"2026-08-12T20:41:09.183083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.187115Z","title":"Beyond conventional ferromagnetism and antiferromagnetism: A phase with nonrelativistic spin and crystal rotation symmetry,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.187115Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:64510f770f256474804bfb4a993bc1792bbc86af68af552e25cc273daf9de77d","observation_id":"df0c3975-9ec2-46f7-bb0d-3d2718becb63","resolution":{"observed_at":"2026-08-12T20:41:09.187115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.191104Z","title":"Editorial: Altermagnetism—a new punch line of fundamental magnetism,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.191104Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:3bdcfef015c613f58f0932f988fd7b34fc8efd2ae305227ff89347014efbaac0","observation_id":"6aa8c782-439a-44b3-ae90-5ae5ca3687eb","resolution":{"observed_at":"2026-08-12T20:41:09.191104Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.194999Z","title":"Momentum- dependent spin splitting by collinear antiferromagnetic ordering,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.194999Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:9e04aa169640926b05eb071adabc934e1093cc93b6789066898d86f3774d1e5a","observation_id":"8f9873dd-7e11-4d0d-b243-f9ba12188f3e","resolution":{"observed_at":"2026-08-12T20:41:09.194999Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.199238Z","title":"Crystal time-reversal symmetry breaking and spontaneous Hall effect in collinear antiferromagnets,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.199238Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:9ac193d169460f3051bf43026dcf2d006828602b6f7c76d57b4017d134e709b3","observation_id":"0c84065b-e36e-4c20-adee-febdda695eb4","resolution":{"observed_at":"2026-08-12T20:41:09.199238Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.203238Z","title":"Giant momentum-dependent spin splitting in centrosymmetric low- Z antiferromagnets,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.203238Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:a1e2fd190507767b107ebba027b6c4e5c5b1e6a9ed1ec7f7a5866ff13d3f13aa","observation_id":"7f0c8bca-6f8c-42c2-84b1-3e2751a5a4bd","resolution":{"observed_at":"2026-08-12T20:41:09.203238Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.207238Z","title":"Prediction of unconventional magnetism in doped FeSb 2,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.207238Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:08658f9a0ad8561ab0479c8d0bd07f94ecc0c828a1c99e7a582ac96d53b5c304","observation_id":"e1c53e77-f457-492b-bdd0-65bcd4523982","resolution":{"observed_at":"2026-08-12T20:41:09.207238Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.10034","last_updated":"2024-09-16T06:52:21Z","snapshot_observed_at":"2026-08-16T13:18:17.838942Z","submitted_at":"2024-09-16T06:52:21Z","title":"Altermagnets and beyond: Nodal magnetically-ordered phases","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.10034","snapshot_observed_at":"2026-08-12T20:41:09.211501Z","title":"Altermagnets and beyond: Nodal magnetically-ordered phases,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.211501Z"},"links":{"cited_paper":"/paper/2409.10034","citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:901dc24bebfd9179cf60d164ecb6a53920f8e94f71008a3d2e3001009d3d1077","observation_id":"e1a68ec5-8c98-45e8-8533-5a2b257ae2e5","resolution":{"observed_at":"2026-08-12T20:41:09.211501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1103/physrevb.103.245127","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:10.383790Z","title":"Topological correspondence between magnetic space group representations and subdimensions,","venue":null,"work_id":"f83aaa4e-8f09-4eeb-8179-6a00708fc958","year":2021},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.215876Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:1e4d294eccf4595be98d46f9cbd228938dfe96ce296f5af48dd62713fdcf0a28","observation_id":"b792646f-c594-4954-9a50-7588ee797391","resolution":{"observed_at":"2026-08-12T20:41:10.390285Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.219882Z","title":"Hubert and R","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.219882Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:cc0bdfdce3096c22b4a3bd474c05548f8f981019d8e16effb65c71154e9dee53","observation_id":"30d93565-60da-4730-a31f-b839130d55f2","resolution":{"observed_at":"2026-08-12T20:41:09.219882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.224016Z","title":"Giant and Tunneling Magne- toresistance in Unconventional Collinear Antiferromagnets with Nonrelativistic Spin-Momentum Coupling,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.224016Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:dca49dcb5c039183d79710196ba1fa417d1b4bfdee9fa04ec489679fc64ccc3c","observation_id":"1d4ebef3-436f-4ff6-9958-c278a4449739","resolution":{"observed_at":"2026-08-12T20:41:09.224016Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:41:09.228296Z","title":"Topological superconductivity in two-dimensional altermagnetic metals,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future","version":4},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-12T20:41:09.228296Z"},"links":{"citing_paper":"/paper/2411.09429"},"observation_digest":"sha256:01eb1bdadd5a338a85e3d0d24cfbee61bede5df86159b72d01bf39fbdd870901","observation_id":"d2e21a36-e765-4939-a6da-4f2b114f267b","resolution":{"observed_at":"2026-08-12T20:41:09.228296Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.09429","last_updated":"2025-02-20T03:47:54Z","latest_version":4,"primary_category":"cond-mat.mtrl-sci","snapshot_observed_at":"2026-08-14T06:00:13.138446Z","submitted_at":"2024-11-14T13:25:04Z","title":"AI-driven inverse design of materials: Past, present and future"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":95,"verified_exact":5,"verified_fuzzy":0},"total_outbound_references":299},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 100 of 299 outbound references and 2 inbound Pith citation observations for arXiv:2411.09429."}