{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:GPLYXXB2P5XZRU52BAQRYKPJGP","short_pith_number":"pith:GPLYXXB2","canonical_record":{"source":{"id":"2411.14159","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2024-11-21T14:19:40Z","cross_cats_sorted":[],"title_canon_sha256":"c34f49f343f329502613c4229590dc56dfb6370038936f2c49e11e1bfe1ac6c6","abstract_canon_sha256":"bcfde3f1fa65e96d1b23ced3261bb918254cbb21051890f928c199fa7a3be45e"},"schema_version":"1.0"},"canonical_sha256":"33d78bdc3a7f6f98d3ba08211c29e933d898eb2d266a45bdf6110925fcfdbd1a","source":{"kind":"arxiv","id":"2411.14159","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.14159","created_at":"2026-07-05T10:47:33Z"},{"alias_kind":"arxiv_version","alias_value":"2411.14159v2","created_at":"2026-07-05T10:47:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14159","created_at":"2026-07-05T10:47:33Z"},{"alias_kind":"pith_short_12","alias_value":"GPLYXXB2P5XZ","created_at":"2026-07-05T10:47:33Z"},{"alias_kind":"pith_short_16","alias_value":"GPLYXXB2P5XZRU52","created_at":"2026-07-05T10:47:33Z"},{"alias_kind":"pith_short_8","alias_value":"GPLYXXB2","created_at":"2026-07-05T10:47:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:GPLYXXB2P5XZRU52BAQRYKPJGP","target":"record","payload":{"canonical_record":{"source":{"id":"2411.14159","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2024-11-21T14:19:40Z","cross_cats_sorted":[],"title_canon_sha256":"c34f49f343f329502613c4229590dc56dfb6370038936f2c49e11e1bfe1ac6c6","abstract_canon_sha256":"bcfde3f1fa65e96d1b23ced3261bb918254cbb21051890f928c199fa7a3be45e"},"schema_version":"1.0"},"canonical_sha256":"33d78bdc3a7f6f98d3ba08211c29e933d898eb2d266a45bdf6110925fcfdbd1a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:47:33.655295Z","signature_b64":"kPzNyc6JK/FmoMawg/ilxwx2IKx/nRV2wVifHDnCHzIu1NBSW80JvAFkCBL4rrvMuf02VlPEWSdCLfTQ42V4AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33d78bdc3a7f6f98d3ba08211c29e933d898eb2d266a45bdf6110925fcfdbd1a","last_reissued_at":"2026-07-05T10:47:33.654744Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:47:33.654744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.14159","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":"2026-07-05T10:47:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z508lpMIurZzRHz//+mkXHtXrzOtExYdU9JD+fb7kYE98n7T2Qx5tKlp8rzg1mgi6YgRcSBpZz7FSc+4m+UKCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T22:36:30.364435Z"},"content_sha256":"407761e6357e682fd8706d4842e2784d9863bebd69b746ab3920aabc58868a85","schema_version":"1.0","event_id":"sha256:407761e6357e682fd8706d4842e2784d9863bebd69b746ab3920aabc58868a85"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:GPLYXXB2P5XZRU52BAQRYKPJGP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Predicting rigidity and connectivity percolation in disordered particulate networks using graph neural networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.comp-ph","authors_text":"D. A. Head","submitted_at":"2024-11-21T14:19:40Z","abstract_excerpt":"Graph neural networks can accurately predict the chemical properties of many molecular systems, but their suitability for large, macromolecular assemblies such as gels is unknown. Here, graph neural networks were trained and optimised for two large-scale classification problems: the rigidity of a molecular network, and the connectivity percolation status which is non-trivial to determine for systems with periodic boundaries. Models trained on lattice systems were found to achieve accuracies >95% for rigidity classification, with slightly lower scores for connectivity percolation due to the inh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14159","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.14159/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:47:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sxy8NNQPf5ICuWrIod0Utk7MFC0Wa7FuNP/TckkC8Nrsm3VCp/tW5usTlAeX3Jqmw/+nvM4eLZhasSwI0SsHBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T22:36:30.364963Z"},"content_sha256":"e58492efaf0a999c85e58ca33bc3a507b1d86333fd948d4470d977377059253d","schema_version":"1.0","event_id":"sha256:e58492efaf0a999c85e58ca33bc3a507b1d86333fd948d4470d977377059253d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GPLYXXB2P5XZRU52BAQRYKPJGP/bundle.json","state_url":"https://pith.science/pith/GPLYXXB2P5XZRU52BAQRYKPJGP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GPLYXXB2P5XZRU52BAQRYKPJGP/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":"2026-08-12T22:36:30Z","links":{"resolver":"https://pith.science/pith/GPLYXXB2P5XZRU52BAQRYKPJGP","bundle":"https://pith.science/pith/GPLYXXB2P5XZRU52BAQRYKPJGP/bundle.json","state":"https://pith.science/pith/GPLYXXB2P5XZRU52BAQRYKPJGP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GPLYXXB2P5XZRU52BAQRYKPJGP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GPLYXXB2P5XZRU52BAQRYKPJGP","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":"bcfde3f1fa65e96d1b23ced3261bb918254cbb21051890f928c199fa7a3be45e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2024-11-21T14:19:40Z","title_canon_sha256":"c34f49f343f329502613c4229590dc56dfb6370038936f2c49e11e1bfe1ac6c6"},"schema_version":"1.0","source":{"id":"2411.14159","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.14159","created_at":"2026-07-05T10:47:33Z"},{"alias_kind":"arxiv_version","alias_value":"2411.14159v2","created_at":"2026-07-05T10:47:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14159","created_at":"2026-07-05T10:47:33Z"},{"alias_kind":"pith_short_12","alias_value":"GPLYXXB2P5XZ","created_at":"2026-07-05T10:47:33Z"},{"alias_kind":"pith_short_16","alias_value":"GPLYXXB2P5XZRU52","created_at":"2026-07-05T10:47:33Z"},{"alias_kind":"pith_short_8","alias_value":"GPLYXXB2","created_at":"2026-07-05T10:47:33Z"}],"graph_snapshots":[{"event_id":"sha256:e58492efaf0a999c85e58ca33bc3a507b1d86333fd948d4470d977377059253d","target":"graph","created_at":"2026-07-05T10:47:33Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2411.14159/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph neural networks can accurately predict the chemical properties of many molecular systems, but their suitability for large, macromolecular assemblies such as gels is unknown. Here, graph neural networks were trained and optimised for two large-scale classification problems: the rigidity of a molecular network, and the connectivity percolation status which is non-trivial to determine for systems with periodic boundaries. Models trained on lattice systems were found to achieve accuracies >95% for rigidity classification, with slightly lower scores for connectivity percolation due to the inh","authors_text":"D. A. Head","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2024-11-21T14:19:40Z","title":"Predicting rigidity and connectivity percolation in disordered particulate networks using graph neural networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14159","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:407761e6357e682fd8706d4842e2784d9863bebd69b746ab3920aabc58868a85","target":"record","created_at":"2026-07-05T10:47:33Z","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":"bcfde3f1fa65e96d1b23ced3261bb918254cbb21051890f928c199fa7a3be45e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2024-11-21T14:19:40Z","title_canon_sha256":"c34f49f343f329502613c4229590dc56dfb6370038936f2c49e11e1bfe1ac6c6"},"schema_version":"1.0","source":{"id":"2411.14159","kind":"arxiv","version":2}},"canonical_sha256":"33d78bdc3a7f6f98d3ba08211c29e933d898eb2d266a45bdf6110925fcfdbd1a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"33d78bdc3a7f6f98d3ba08211c29e933d898eb2d266a45bdf6110925fcfdbd1a","first_computed_at":"2026-07-05T10:47:33.654744Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:47:33.654744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kPzNyc6JK/FmoMawg/ilxwx2IKx/nRV2wVifHDnCHzIu1NBSW80JvAFkCBL4rrvMuf02VlPEWSdCLfTQ42V4AA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:47:33.655295Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.14159","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:407761e6357e682fd8706d4842e2784d9863bebd69b746ab3920aabc58868a85","sha256:e58492efaf0a999c85e58ca33bc3a507b1d86333fd948d4470d977377059253d"],"state_sha256":"6f8364c96cd7a9bfceff5ac25229e38391b3dcdbdb020faa14b20ea80c9ffdb1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1x1BxRKknwX/F2opbdOpi2BFWLHFFJ4K69R/2ezjaZVw6a6ZcWwgdeTK9XcijcQ06u4QGLHTVvCwR205hPkTDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T22:36:30.369967Z","bundle_sha256":"3abd8ce5f1910b5cfc34f0f30630f80f3f6edd10f2ff9d8e560412345651cdf2"}}