{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:HGABAW4VNFA7HGY32GNJE3TRFS","short_pith_number":"pith:HGABAW4V","schema_version":"1.0","canonical_sha256":"3980105b956941f39b1bd19a926e712c8212bdf44caec2d0a02861e637c551ab","source":{"kind":"arxiv","id":"1810.06173","version":3},"attestation_state":"computed","paper":{"title":"Learning heterogenous reaction rates from stochastic simulations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.chem-ph","physics.comp-ph"],"primary_cat":"cond-mat.soft","authors_text":"Ariana Torres-Knoop, Ivan Kryven","submitted_at":"2018-10-09T19:30:10Z","abstract_excerpt":"Reaction rate equations are ordinary differential equations that are frequently used to describe deterministic chemical kinetics at the macroscopic scale. At the microscopic scale, the chemical kinetics is stochastic and can be captured by complex dynamical systems reproducing spatial movements of molecules and their collisions. Such molecular dynamics systems may implicitly capture intricate phenomena that affect reaction rates but are not accounted for in the macroscopic models. In this work we present a data assimilation procedure for learning non-homogenous kinetic parameters from molecula"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1810.06173","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.soft","submitted_at":"2018-10-09T19:30:10Z","cross_cats_sorted":["physics.chem-ph","physics.comp-ph"],"title_canon_sha256":"0d3a1811a4d6e3ae1336fd429630070e497525f9af553bfef179ab5c0e476034","abstract_canon_sha256":"371e0ffc6b173ad7d52b5f0bf1b1369b43217ffe74fcc05c76dfe2eba0140fbf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:39:34.396999Z","signature_b64":"BJvjcT8+4+WsIQmBb7SdnftAPgLZmJH2+6lZfJcW2+McOMfmjsED4g5isMS1HWlHRUqvpeKTi9tVCB9qDMu/Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3980105b956941f39b1bd19a926e712c8212bdf44caec2d0a02861e637c551ab","last_reissued_at":"2026-07-05T02:39:34.396524Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:39:34.396524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning heterogenous reaction rates from stochastic simulations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.chem-ph","physics.comp-ph"],"primary_cat":"cond-mat.soft","authors_text":"Ariana Torres-Knoop, Ivan Kryven","submitted_at":"2018-10-09T19:30:10Z","abstract_excerpt":"Reaction rate equations are ordinary differential equations that are frequently used to describe deterministic chemical kinetics at the macroscopic scale. At the microscopic scale, the chemical kinetics is stochastic and can be captured by complex dynamical systems reproducing spatial movements of molecules and their collisions. Such molecular dynamics systems may implicitly capture intricate phenomena that affect reaction rates but are not accounted for in the macroscopic models. In this work we present a data assimilation procedure for learning non-homogenous kinetic parameters from molecula"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.06173","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1810.06173/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1810.06173","created_at":"2026-07-05T02:39:34.396589+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.06173v3","created_at":"2026-07-05T02:39:34.396589+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.06173","created_at":"2026-07-05T02:39:34.396589+00:00"},{"alias_kind":"pith_short_12","alias_value":"HGABAW4VNFA7","created_at":"2026-07-05T02:39:34.396589+00:00"},{"alias_kind":"pith_short_16","alias_value":"HGABAW4VNFA7HGY3","created_at":"2026-07-05T02:39:34.396589+00:00"},{"alias_kind":"pith_short_8","alias_value":"HGABAW4V","created_at":"2026-07-05T02:39:34.396589+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HGABAW4VNFA7HGY32GNJE3TRFS","json":"https://pith.science/pith/HGABAW4VNFA7HGY32GNJE3TRFS.json","graph_json":"https://pith.science/api/pith-number/HGABAW4VNFA7HGY32GNJE3TRFS/graph.json","events_json":"https://pith.science/api/pith-number/HGABAW4VNFA7HGY32GNJE3TRFS/events.json","paper":"https://pith.science/paper/HGABAW4V"},"agent_actions":{"view_html":"https://pith.science/pith/HGABAW4VNFA7HGY32GNJE3TRFS","download_json":"https://pith.science/pith/HGABAW4VNFA7HGY32GNJE3TRFS.json","view_paper":"https://pith.science/paper/HGABAW4V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.06173&json=true","fetch_graph":"https://pith.science/api/pith-number/HGABAW4VNFA7HGY32GNJE3TRFS/graph.json","fetch_events":"https://pith.science/api/pith-number/HGABAW4VNFA7HGY32GNJE3TRFS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HGABAW4VNFA7HGY32GNJE3TRFS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HGABAW4VNFA7HGY32GNJE3TRFS/action/storage_attestation","attest_author":"https://pith.science/pith/HGABAW4VNFA7HGY32GNJE3TRFS/action/author_attestation","sign_citation":"https://pith.science/pith/HGABAW4VNFA7HGY32GNJE3TRFS/action/citation_signature","submit_replication":"https://pith.science/pith/HGABAW4VNFA7HGY32GNJE3TRFS/action/replication_record"}},"created_at":"2026-07-05T02:39:34.396589+00:00","updated_at":"2026-07-05T02:39:34.396589+00:00"}