{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:Z572OEMDW7JWCCGNLJFI725CAC","short_pith_number":"pith:Z572OEMD","schema_version":"1.0","canonical_sha256":"cf7fa71183b7d36108cd5a4a8feba200b354a5789ef75d4e112caf97aa0bca1a","source":{"kind":"arxiv","id":"1903.08615","version":2},"attestation_state":"computed","paper":{"title":"Scaling methods for accelerating kinetic Monte Carlo simulations of chemical reaction networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.chem-ph","q-bio.MN"],"primary_cat":"q-bio.QM","authors_text":"Song Feng, William S. Hlavacek, Yen Ting Lin","submitted_at":"2019-03-20T16:58:14Z","abstract_excerpt":"Various kinetic Monte Carlo algorithms become inefficient when some of the population sizes in a system are large, which gives rise to a large number of reaction events per unit time. Here, we present a new acceleration algorithm based on adaptive and heterogeneous scaling of reaction rates and stoichiometric coefficients. The algorithm is conceptually related to the commonly used idea of accelerating a stochastic simulation by considering a sub-volume $\\lambda \\Omega$ ($0<\\lambda<1$) within a system of interest, which reduces the number of reaction events per unit time occurring in a simulati"},"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":"1903.08615","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2019-03-20T16:58:14Z","cross_cats_sorted":["physics.chem-ph","q-bio.MN"],"title_canon_sha256":"ac365b08aa9b7a929999d4d5d36b1c5524bb43914a3b8ea77b5b66c60059030d","abstract_canon_sha256":"5ce948667ae09b962d564ae72f8d9231db0631777697b514f85bde3430d3d77f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:44.741458Z","signature_b64":"8tYQP8/LTuNcjkiK1PmWVrwtgYErmbSSZvUA9//3div9aYG62K976cE3y96O+AgZkfZMbtwHrtV8Yt67QC2PAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf7fa71183b7d36108cd5a4a8feba200b354a5789ef75d4e112caf97aa0bca1a","last_reissued_at":"2026-05-17T23:39:44.740605Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:44.740605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scaling methods for accelerating kinetic Monte Carlo simulations of chemical reaction networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.chem-ph","q-bio.MN"],"primary_cat":"q-bio.QM","authors_text":"Song Feng, William S. Hlavacek, Yen Ting Lin","submitted_at":"2019-03-20T16:58:14Z","abstract_excerpt":"Various kinetic Monte Carlo algorithms become inefficient when some of the population sizes in a system are large, which gives rise to a large number of reaction events per unit time. Here, we present a new acceleration algorithm based on adaptive and heterogeneous scaling of reaction rates and stoichiometric coefficients. The algorithm is conceptually related to the commonly used idea of accelerating a stochastic simulation by considering a sub-volume $\\lambda \\Omega$ ($0<\\lambda<1$) within a system of interest, which reduces the number of reaction events per unit time occurring in a simulati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.08615","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":""},"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":"1903.08615","created_at":"2026-05-17T23:39:44.740760+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.08615v2","created_at":"2026-05-17T23:39:44.740760+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.08615","created_at":"2026-05-17T23:39:44.740760+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z572OEMDW7JW","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z572OEMDW7JWCCGN","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z572OEMD","created_at":"2026-05-18T12:33:33.725879+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/Z572OEMDW7JWCCGNLJFI725CAC","json":"https://pith.science/pith/Z572OEMDW7JWCCGNLJFI725CAC.json","graph_json":"https://pith.science/api/pith-number/Z572OEMDW7JWCCGNLJFI725CAC/graph.json","events_json":"https://pith.science/api/pith-number/Z572OEMDW7JWCCGNLJFI725CAC/events.json","paper":"https://pith.science/paper/Z572OEMD"},"agent_actions":{"view_html":"https://pith.science/pith/Z572OEMDW7JWCCGNLJFI725CAC","download_json":"https://pith.science/pith/Z572OEMDW7JWCCGNLJFI725CAC.json","view_paper":"https://pith.science/paper/Z572OEMD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.08615&json=true","fetch_graph":"https://pith.science/api/pith-number/Z572OEMDW7JWCCGNLJFI725CAC/graph.json","fetch_events":"https://pith.science/api/pith-number/Z572OEMDW7JWCCGNLJFI725CAC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z572OEMDW7JWCCGNLJFI725CAC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z572OEMDW7JWCCGNLJFI725CAC/action/storage_attestation","attest_author":"https://pith.science/pith/Z572OEMDW7JWCCGNLJFI725CAC/action/author_attestation","sign_citation":"https://pith.science/pith/Z572OEMDW7JWCCGNLJFI725CAC/action/citation_signature","submit_replication":"https://pith.science/pith/Z572OEMDW7JWCCGNLJFI725CAC/action/replication_record"}},"created_at":"2026-05-17T23:39:44.740760+00:00","updated_at":"2026-05-17T23:39:44.740760+00:00"}