{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:O2JXGK7ICTEJQC7QRW4W2LWN4Q","short_pith_number":"pith:O2JXGK7I","canonical_record":{"source":{"id":"2405.04967","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-05-08T11:13:30Z","cross_cats_sorted":[],"title_canon_sha256":"fe73b92143d58b74e4ce7624aeca3245dc503f87cca79e6c993f75ae26279602","abstract_canon_sha256":"8bc543292a7cfc72cadc95f938e63ac652c84cc2ebf9223f33e05634a5a2188e"},"schema_version":"1.0"},"canonical_sha256":"7693732be814c8980bf08db96d2ecde4084e9dde051e715a719748569ac5be28","source":{"kind":"arxiv","id":"2405.04967","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.04967","created_at":"2026-05-17T23:38:46Z"},{"alias_kind":"arxiv_version","alias_value":"2405.04967v2","created_at":"2026-05-17T23:38:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.04967","created_at":"2026-05-17T23:38:46Z"},{"alias_kind":"pith_short_12","alias_value":"O2JXGK7ICTEJ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"O2JXGK7ICTEJQC7Q","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"O2JXGK7I","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:O2JXGK7ICTEJQC7QRW4W2LWN4Q","target":"record","payload":{"canonical_record":{"source":{"id":"2405.04967","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-05-08T11:13:30Z","cross_cats_sorted":[],"title_canon_sha256":"fe73b92143d58b74e4ce7624aeca3245dc503f87cca79e6c993f75ae26279602","abstract_canon_sha256":"8bc543292a7cfc72cadc95f938e63ac652c84cc2ebf9223f33e05634a5a2188e"},"schema_version":"1.0"},"canonical_sha256":"7693732be814c8980bf08db96d2ecde4084e9dde051e715a719748569ac5be28","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:46.143538Z","signature_b64":"6wdN3L+TRl/PBED6thyIr7a67f6ah3NCY1NL4OboIBPGiCB0WNqnmtrGtuHl4YUB/X9MOztiajeHGcGRPZmeDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7693732be814c8980bf08db96d2ecde4084e9dde051e715a719748569ac5be28","last_reissued_at":"2026-05-17T23:38:46.142985Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:46.142985Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.04967","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WrMx94BxPTPUcw1MOQsyVyX78IvMu31Uf4hX6TL6Ol+HlWpzxYrPm+tFs0XxYN2z2hqPWyNlbS4xjKF2HON5DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T08:56:03.667194Z"},"content_sha256":"f2067c0e847bd46e54a48e631acc47e5aadc8bce4ab034d6c2a260e5ad9dce29","schema_version":"1.0","event_id":"sha256:f2067c0e847bd46e54a48e631acc47e5aadc8bce4ab034d6c2a260e5ad9dce29"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:O2JXGK7ICTEJQC7QRW4W2LWN4Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"MatterSim predicts Gibbs free energies of inorganic solids at near first-principles accuracy across wide temperatures and pressures.","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Andrew Fowler, Chang Liu, Chenxi Hu, Claudio Zeni, Daniel Z\\\"ugner, Guanzhi Li, Han Yang, Hongxia Hao, Jake Smith, Jielan Li, Lingyu Kong, Lixin Sun, Matthew Horton, Qian Wang, Robert Pinsler, Shuizhou Chen, Tian Xie, Xixian Liu, Yichi Zhou, Yu Shi, Zekun Chen, Ziheng Lu","submitted_at":"2024-05-08T11:13:30Z","abstract_excerpt":"Accurate and fast prediction of materials properties is central to the digital transformation of materials design. However, the vast design space and diverse operating conditions pose significant challenges for accurately modeling arbitrary material candidates and forecasting their properties. We present MatterSim, a deep learning model actively learned from large-scale first-principles computations, for efficient atomistic simulations at first-principles level and accurate prediction of broad material properties across the periodic table, spanning temperatures from 0 to 5000 K and pressures u"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"MatterSim predicts Gibbs free energies for a wide range of inorganic solids with near-first-principles accuracy and achieves a 15 meV/atom resolution for temperatures up to 1000 K compared with experiments.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The training data from first-principles computations sufficiently covers the relevant chemical space, temperatures, and pressures so that the model generalizes accurately to unseen compositions and conditions without large extrapolation errors.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MatterSim delivers a single deep learning force field that simulates inorganic materials across elements, 0-5000 K, and up to 1000 GPa with near first-principles accuracy for lattice dynamics, mechanics, and Gibbs free energies.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"MatterSim predicts Gibbs free energies of inorganic solids at near first-principles accuracy across wide temperatures and pressures.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"00593ce9a9cfd326161b61bfa0c254b59ba6aa744b089994756b5c82d7ac2278"},"source":{"id":"2405.04967","kind":"arxiv","version":2},"verdict":{"id":"d8c257a0-bc91-45f2-99c5-768ff5222ba4","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T00:06:16.986880Z","strongest_claim":"MatterSim predicts Gibbs free energies for a wide range of inorganic solids with near-first-principles accuracy and achieves a 15 meV/atom resolution for temperatures up to 1000 K compared with experiments.","one_line_summary":"MatterSim delivers a single deep learning force field that simulates inorganic materials across elements, 0-5000 K, and up to 1000 GPa with near first-principles accuracy for lattice dynamics, mechanics, and Gibbs free energies.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The training data from first-principles computations sufficiently covers the relevant chemical space, temperatures, and pressures so that the model generalizes accurately to unseen compositions and conditions without large extrapolation errors.","pith_extraction_headline":"MatterSim predicts Gibbs free energies of inorganic solids at near first-principles accuracy across wide temperatures and pressures."},"references":{"count":158,"sample":[{"doi":"","year":2014,"title":"G. Fiori, F. Bonaccorso, G. Iannaccone, T. Palacios, D. Neumaier, A. Seabaugh, S.K. Banerjee, L. Colombo, Electronics based on two-dimensional materials. Nature nanotechnology 9(10), 768–779 (2014)","work_id":"9e8b363c-a620-4715-8cf3-ad1f5f367ca8","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2007,"title":"T. Li, G. Galli, Electronic properties of mos2 nanoparticles. The Journal of Physical Chemistry C 111(44), 16192–16196 (2007)","work_id":"36bb55a6-730a-435a-b304-2f10d9c30c4b","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1980,"title":"K. Mizushima, P. Jones, P. Wiseman, J.B. Goodenough, Lixcoo2 (0¡ x¡-1): A new cathode material for batteries of high energy density. Materials Research Bulletin 15(6), 783–789 (1980)","work_id":"132485bf-3f8d-46bd-9bd7-2904bf1c3bdd","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1998,"title":"G. Ceder, Y.M. Chiang, D. Sadoway, M. Aydinol, Y.I. Jang, B. Huang, Identification of cathode materials for lithium batteries guided by first-principles calculations. Nature 392(6677), 694– 696 (1998)","work_id":"2d6fd784-b723-4b7b-813c-e928d6d6c1e2","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2015,"title":"M.W. Tibbitt, C.B. Rodell, J.A. Burdick, K.S. Anseth, Progress in material design for biomed- ical applications. Proceedings of the National Academy of Sciences 112(47), 14444–14451 (2015)","work_id":"24f190e2-6ff3-48fa-81fb-d1b3b6a6623a","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":158,"snapshot_sha256":"f1816269224527508b600e0d1c5fbe958b46053fd3c96b330f64842d3bfa0c7e","internal_anchors":2},"formal_canon":{"evidence_count":2,"snapshot_sha256":"4e896952d02dc964eadc8c52157c8e2b4a8a5920b271b7636f411aba63e92887"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"d8c257a0-bc91-45f2-99c5-768ff5222ba4"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wF2mOXZ2b/7ZpsnuXmvrRqbCLHkLQRyqJ0ye4l9mo08CfD+rW8ZVq6Sz48u5f7wDHwZXKRKF3MHryG24yKXOBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T08:56:03.668872Z"},"content_sha256":"3f5f858fb738190bce3e7e60228528219efe4690d505fd63ae7b630e1b080e5a","schema_version":"1.0","event_id":"sha256:3f5f858fb738190bce3e7e60228528219efe4690d505fd63ae7b630e1b080e5a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O2JXGK7ICTEJQC7QRW4W2LWN4Q/bundle.json","state_url":"https://pith.science/pith/O2JXGK7ICTEJQC7QRW4W2LWN4Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O2JXGK7ICTEJQC7QRW4W2LWN4Q/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-23T08:56:03Z","links":{"resolver":"https://pith.science/pith/O2JXGK7ICTEJQC7QRW4W2LWN4Q","bundle":"https://pith.science/pith/O2JXGK7ICTEJQC7QRW4W2LWN4Q/bundle.json","state":"https://pith.science/pith/O2JXGK7ICTEJQC7QRW4W2LWN4Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O2JXGK7ICTEJQC7QRW4W2LWN4Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:O2JXGK7ICTEJQC7QRW4W2LWN4Q","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8bc543292a7cfc72cadc95f938e63ac652c84cc2ebf9223f33e05634a5a2188e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-05-08T11:13:30Z","title_canon_sha256":"fe73b92143d58b74e4ce7624aeca3245dc503f87cca79e6c993f75ae26279602"},"schema_version":"1.0","source":{"id":"2405.04967","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.04967","created_at":"2026-05-17T23:38:46Z"},{"alias_kind":"arxiv_version","alias_value":"2405.04967v2","created_at":"2026-05-17T23:38:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.04967","created_at":"2026-05-17T23:38:46Z"},{"alias_kind":"pith_short_12","alias_value":"O2JXGK7ICTEJ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"O2JXGK7ICTEJQC7Q","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"O2JXGK7I","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:3f5f858fb738190bce3e7e60228528219efe4690d505fd63ae7b630e1b080e5a","target":"graph","created_at":"2026-05-17T23:38:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"MatterSim predicts Gibbs free energies for a wide range of inorganic solids with near-first-principles accuracy and achieves a 15 meV/atom resolution for temperatures up to 1000 K compared with experiments."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The training data from first-principles computations sufficiently covers the relevant chemical space, temperatures, and pressures so that the model generalizes accurately to unseen compositions and conditions without large extrapolation errors."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"MatterSim delivers a single deep learning force field that simulates inorganic materials across elements, 0-5000 K, and up to 1000 GPa with near first-principles accuracy for lattice dynamics, mechanics, and Gibbs free energies."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"MatterSim predicts Gibbs free energies of inorganic solids at near first-principles accuracy across wide temperatures and pressures."}],"snapshot_sha256":"00593ce9a9cfd326161b61bfa0c254b59ba6aa744b089994756b5c82d7ac2278"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"4e896952d02dc964eadc8c52157c8e2b4a8a5920b271b7636f411aba63e92887"},"paper":{"abstract_excerpt":"Accurate and fast prediction of materials properties is central to the digital transformation of materials design. However, the vast design space and diverse operating conditions pose significant challenges for accurately modeling arbitrary material candidates and forecasting their properties. We present MatterSim, a deep learning model actively learned from large-scale first-principles computations, for efficient atomistic simulations at first-principles level and accurate prediction of broad material properties across the periodic table, spanning temperatures from 0 to 5000 K and pressures u","authors_text":"Andrew Fowler, Chang Liu, Chenxi Hu, Claudio Zeni, Daniel Z\\\"ugner, Guanzhi Li, Han Yang, Hongxia Hao, Jake Smith, Jielan Li, Lingyu Kong, Lixin Sun, Matthew Horton, Qian Wang, Robert Pinsler, Shuizhou Chen, Tian Xie, Xixian Liu, Yichi Zhou, Yu Shi, Zekun Chen, Ziheng Lu","cross_cats":[],"headline":"MatterSim predicts Gibbs free energies of inorganic solids at near first-principles accuracy across wide temperatures and pressures.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-05-08T11:13:30Z","title":"MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures"},"references":{"count":158,"internal_anchors":2,"resolved_work":158,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"G. Fiori, F. Bonaccorso, G. Iannaccone, T. Palacios, D. Neumaier, A. Seabaugh, S.K. Banerjee, L. Colombo, Electronics based on two-dimensional materials. Nature nanotechnology 9(10), 768–779 (2014)","work_id":"9e8b363c-a620-4715-8cf3-ad1f5f367ca8","year":2014},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"T. Li, G. Galli, Electronic properties of mos2 nanoparticles. The Journal of Physical Chemistry C 111(44), 16192–16196 (2007)","work_id":"36bb55a6-730a-435a-b304-2f10d9c30c4b","year":2007},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"K. Mizushima, P. Jones, P. Wiseman, J.B. Goodenough, Lixcoo2 (0¡ x¡-1): A new cathode material for batteries of high energy density. Materials Research Bulletin 15(6), 783–789 (1980)","work_id":"132485bf-3f8d-46bd-9bd7-2904bf1c3bdd","year":1980},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"G. Ceder, Y.M. Chiang, D. Sadoway, M. Aydinol, Y.I. Jang, B. Huang, Identification of cathode materials for lithium batteries guided by first-principles calculations. Nature 392(6677), 694– 696 (1998)","work_id":"2d6fd784-b723-4b7b-813c-e928d6d6c1e2","year":1998},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"M.W. Tibbitt, C.B. Rodell, J.A. Burdick, K.S. Anseth, Progress in material design for biomed- ical applications. Proceedings of the National Academy of Sciences 112(47), 14444–14451 (2015)","work_id":"24f190e2-6ff3-48fa-81fb-d1b3b6a6623a","year":2015}],"snapshot_sha256":"f1816269224527508b600e0d1c5fbe958b46053fd3c96b330f64842d3bfa0c7e"},"source":{"id":"2405.04967","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-17T00:06:16.986880Z","id":"d8c257a0-bc91-45f2-99c5-768ff5222ba4","model_set":{"reader":"grok-4.3"},"one_line_summary":"MatterSim delivers a single deep learning force field that simulates inorganic materials across elements, 0-5000 K, and up to 1000 GPa with near first-principles accuracy for lattice dynamics, mechanics, and Gibbs free energies.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"MatterSim predicts Gibbs free energies of inorganic solids at near first-principles accuracy across wide temperatures and pressures.","strongest_claim":"MatterSim predicts Gibbs free energies for a wide range of inorganic solids with near-first-principles accuracy and achieves a 15 meV/atom resolution for temperatures up to 1000 K compared with experiments.","weakest_assumption":"The training data from first-principles computations sufficiently covers the relevant chemical space, temperatures, and pressures so that the model generalizes accurately to unseen compositions and conditions without large extrapolation errors."}},"verdict_id":"d8c257a0-bc91-45f2-99c5-768ff5222ba4"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f2067c0e847bd46e54a48e631acc47e5aadc8bce4ab034d6c2a260e5ad9dce29","target":"record","created_at":"2026-05-17T23:38:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8bc543292a7cfc72cadc95f938e63ac652c84cc2ebf9223f33e05634a5a2188e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-05-08T11:13:30Z","title_canon_sha256":"fe73b92143d58b74e4ce7624aeca3245dc503f87cca79e6c993f75ae26279602"},"schema_version":"1.0","source":{"id":"2405.04967","kind":"arxiv","version":2}},"canonical_sha256":"7693732be814c8980bf08db96d2ecde4084e9dde051e715a719748569ac5be28","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7693732be814c8980bf08db96d2ecde4084e9dde051e715a719748569ac5be28","first_computed_at":"2026-05-17T23:38:46.142985Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:46.142985Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6wdN3L+TRl/PBED6thyIr7a67f6ah3NCY1NL4OboIBPGiCB0WNqnmtrGtuHl4YUB/X9MOztiajeHGcGRPZmeDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:46.143538Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.04967","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f2067c0e847bd46e54a48e631acc47e5aadc8bce4ab034d6c2a260e5ad9dce29","sha256:3f5f858fb738190bce3e7e60228528219efe4690d505fd63ae7b630e1b080e5a"],"state_sha256":"be4397f1f2ae8f9fd534aa86a3632e1fcb6acce21a601713b1417a147ce2a3f8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yKEfIektJzZoUXXGKrPwq60vYJ50vvEyIkS8k1K3PNgSHMt1hYpvyNzshXPngzEOEZ4WqelNQ/bN/2cUQHwqCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T08:56:03.674241Z","bundle_sha256":"ee24fdd2c83b2741b087a990dbedc3c93f5a46f32becf8776871ec177f7d13a4"}}