{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:HO5T7PJIEZUQ5KLR5TUGH7BDBB","short_pith_number":"pith:HO5T7PJI","canonical_record":{"source":{"id":"1810.07310","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-16T23:21:03Z","cross_cats_sorted":["cond-mat.mtrl-sci","cs.CE","physics.comp-ph","stat.ML"],"title_canon_sha256":"ffec5e91544e08e02540679f27b25469bb589b66a639fb81d13c187b11b8f7d4","abstract_canon_sha256":"cf233c714b11ec7d4eb0aceef7e1def89edc98325922bc2294a9c0a1e742f99c"},"schema_version":"1.0"},"canonical_sha256":"3bbb3fbd2826690ea971ece863fc230874e854ab27410b25fcafc95f89e3aa89","source":{"kind":"arxiv","id":"1810.07310","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.07310","created_at":"2026-05-17T23:55:07Z"},{"alias_kind":"arxiv_version","alias_value":"1810.07310v3","created_at":"2026-05-17T23:55:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.07310","created_at":"2026-05-17T23:55:07Z"},{"alias_kind":"pith_short_12","alias_value":"HO5T7PJIEZUQ","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HO5T7PJIEZUQ5KLR","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HO5T7PJI","created_at":"2026-05-18T12:32:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:HO5T7PJIEZUQ5KLR5TUGH7BDBB","target":"record","payload":{"canonical_record":{"source":{"id":"1810.07310","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-16T23:21:03Z","cross_cats_sorted":["cond-mat.mtrl-sci","cs.CE","physics.comp-ph","stat.ML"],"title_canon_sha256":"ffec5e91544e08e02540679f27b25469bb589b66a639fb81d13c187b11b8f7d4","abstract_canon_sha256":"cf233c714b11ec7d4eb0aceef7e1def89edc98325922bc2294a9c0a1e742f99c"},"schema_version":"1.0"},"canonical_sha256":"3bbb3fbd2826690ea971ece863fc230874e854ab27410b25fcafc95f89e3aa89","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:07.284130Z","signature_b64":"fP+uPJ7hVFMxhrCtzEruTJUEY/HbcG5bkMd6XwkH+Van/zJPy/Uq9+e+WxZwQoBZ5nVUJjMUw/owOXdm5Mt8Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3bbb3fbd2826690ea971ece863fc230874e854ab27410b25fcafc95f89e3aa89","last_reissued_at":"2026-05-17T23:55:07.283655Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:07.283655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.07310","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:55:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pXmdF6qCi2+8ws5w0wc19xEA855TjZYNr6SUBWOu/Ytg0iOw4YKqqvWEerRVjoUNLE3rFEXNENyrsdRxWt8zBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T02:34:56.079594Z"},"content_sha256":"f1e8d2b4c3804d655d931b9323570d00353e1a40ec352b5bb0af920cbd67118d","schema_version":"1.0","event_id":"sha256:f1e8d2b4c3804d655d931b9323570d00353e1a40ec352b5bb0af920cbd67118d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:HO5T7PJIEZUQ5KLR5TUGH7BDBB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Prediction of Atomization Energy Using Graph Kernel and Active Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","cs.CE","physics.comp-ph","stat.ML"],"primary_cat":"cs.LG","authors_text":"Wibe A. de Jong, Yu-Hang Tang","submitted_at":"2018-10-16T23:21:03Z","abstract_excerpt":"Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management. In this paper, we present a kernel-based pipeline that can learn and predict the atomization energy of molecules with high accuracy. The framework employs Gaussian process regression to perform predictions based on the similarity between molecules, which is computed using the marginalized graph kernel. To apply the marginalized graph kernel, a spatial adjacency rule is first employed to conver"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.07310","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2026-05-17T23:55:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JZd2Zoy/DCwl6Fk0uflwPjd4wxLP9cvSnYXlixhbz4MHg89iqyNwvALeh+RpMe4nuaoHk2ptCPFY0OYeq1KFBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T02:34:56.080233Z"},"content_sha256":"64bd847763116c364eb7e98ba56d84b83445b53931fb3ce2f8f5847e426eb420","schema_version":"1.0","event_id":"sha256:64bd847763116c364eb7e98ba56d84b83445b53931fb3ce2f8f5847e426eb420"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HO5T7PJIEZUQ5KLR5TUGH7BDBB/bundle.json","state_url":"https://pith.science/pith/HO5T7PJIEZUQ5KLR5TUGH7BDBB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HO5T7PJIEZUQ5KLR5TUGH7BDBB/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":"2026-05-27T02:34:56Z","links":{"resolver":"https://pith.science/pith/HO5T7PJIEZUQ5KLR5TUGH7BDBB","bundle":"https://pith.science/pith/HO5T7PJIEZUQ5KLR5TUGH7BDBB/bundle.json","state":"https://pith.science/pith/HO5T7PJIEZUQ5KLR5TUGH7BDBB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HO5T7PJIEZUQ5KLR5TUGH7BDBB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:HO5T7PJIEZUQ5KLR5TUGH7BDBB","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":"cf233c714b11ec7d4eb0aceef7e1def89edc98325922bc2294a9c0a1e742f99c","cross_cats_sorted":["cond-mat.mtrl-sci","cs.CE","physics.comp-ph","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-16T23:21:03Z","title_canon_sha256":"ffec5e91544e08e02540679f27b25469bb589b66a639fb81d13c187b11b8f7d4"},"schema_version":"1.0","source":{"id":"1810.07310","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.07310","created_at":"2026-05-17T23:55:07Z"},{"alias_kind":"arxiv_version","alias_value":"1810.07310v3","created_at":"2026-05-17T23:55:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.07310","created_at":"2026-05-17T23:55:07Z"},{"alias_kind":"pith_short_12","alias_value":"HO5T7PJIEZUQ","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HO5T7PJIEZUQ5KLR","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HO5T7PJI","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:64bd847763116c364eb7e98ba56d84b83445b53931fb3ce2f8f5847e426eb420","target":"graph","created_at":"2026-05-17T23:55:07Z","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"},"paper":{"abstract_excerpt":"Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management. In this paper, we present a kernel-based pipeline that can learn and predict the atomization energy of molecules with high accuracy. The framework employs Gaussian process regression to perform predictions based on the similarity between molecules, which is computed using the marginalized graph kernel. To apply the marginalized graph kernel, a spatial adjacency rule is first employed to conver","authors_text":"Wibe A. de Jong, Yu-Hang Tang","cross_cats":["cond-mat.mtrl-sci","cs.CE","physics.comp-ph","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-16T23:21:03Z","title":"Prediction of Atomization Energy Using Graph Kernel and Active Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.07310","kind":"arxiv","version":3},"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:f1e8d2b4c3804d655d931b9323570d00353e1a40ec352b5bb0af920cbd67118d","target":"record","created_at":"2026-05-17T23:55:07Z","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":"cf233c714b11ec7d4eb0aceef7e1def89edc98325922bc2294a9c0a1e742f99c","cross_cats_sorted":["cond-mat.mtrl-sci","cs.CE","physics.comp-ph","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-16T23:21:03Z","title_canon_sha256":"ffec5e91544e08e02540679f27b25469bb589b66a639fb81d13c187b11b8f7d4"},"schema_version":"1.0","source":{"id":"1810.07310","kind":"arxiv","version":3}},"canonical_sha256":"3bbb3fbd2826690ea971ece863fc230874e854ab27410b25fcafc95f89e3aa89","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3bbb3fbd2826690ea971ece863fc230874e854ab27410b25fcafc95f89e3aa89","first_computed_at":"2026-05-17T23:55:07.283655Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:55:07.283655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fP+uPJ7hVFMxhrCtzEruTJUEY/HbcG5bkMd6XwkH+Van/zJPy/Uq9+e+WxZwQoBZ5nVUJjMUw/owOXdm5Mt8Aw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:55:07.284130Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.07310","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f1e8d2b4c3804d655d931b9323570d00353e1a40ec352b5bb0af920cbd67118d","sha256:64bd847763116c364eb7e98ba56d84b83445b53931fb3ce2f8f5847e426eb420"],"state_sha256":"a7f96eeb872617095916bd80432c7bfb16e14ff8cd465fdec76b49ca7c060a35"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5oxUR/3+AVW83SRpffr+ess4AbDgirWF32fwpz/UI5O4/WHHhySY+LFNb8ORDGbIeul6Kd3T7w1z/SWMYywpDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T02:34:56.082968Z","bundle_sha256":"9ffce6ae253ed1bf13ddd1203fe110938506f7d97cb63dba07a352bc973a7a6e"}}