{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3DFXZYXXQ2ZWNXSM4BWC4VDTOE","short_pith_number":"pith:3DFXZYXX","schema_version":"1.0","canonical_sha256":"d8cb7ce2f786b366de4ce06c2e54737126c230306f997bf9a10c9257d78904d9","source":{"kind":"arxiv","id":"2605.17753","version":1},"attestation_state":"computed","paper":{"title":"Fast and accurate committor estimation for kinetics simulations","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.chem-ph","authors_text":"Hao Wang, Ru Wang, Wenjian Liu, Xiaojun Ji","submitted_at":"2026-05-18T02:14:10Z","abstract_excerpt":"Computing long-timescale kinetics of biomolecular processes remains a major challenge for atomistic simulations. A way out is to exploit local kinetic information to construct the global stationary flux across the reaction space. The committor serves as the optimal reaction coordinate for this purpose; however, its calculation is itself highly demanding. Here, we introduce a fast and accurate algorithm for committor estimation by leveraging highly parallelizable short trajectory simulations and analogue prediction. The resulting committor is represented via a neural network ansatz and subseque"},"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":"2605.17753","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2026-05-18T02:14:10Z","cross_cats_sorted":[],"title_canon_sha256":"66b2dcc21e4feec9459b0fbc824e5e0b312c08092eb8064b26fc2dc1a643e114","abstract_canon_sha256":"c3223a48fef9e535b30396efbdc32adfca0bc301498de6fc71bad4ff3aa13f45"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:56.505754Z","signature_b64":"3hQyed1BMZC6FwG4+wTA27fTCSsIL0kvwq02p2TDGG39S7FtbMrCekWAf1GPDQUcHPp0Es54mEBl5Q+l+zhvBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8cb7ce2f786b366de4ce06c2e54737126c230306f997bf9a10c9257d78904d9","last_reissued_at":"2026-05-20T00:04:56.504876Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:56.504876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast and accurate committor estimation for kinetics simulations","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.chem-ph","authors_text":"Hao Wang, Ru Wang, Wenjian Liu, Xiaojun Ji","submitted_at":"2026-05-18T02:14:10Z","abstract_excerpt":"Computing long-timescale kinetics of biomolecular processes remains a major challenge for atomistic simulations. A way out is to exploit local kinetic information to construct the global stationary flux across the reaction space. The committor serves as the optimal reaction coordinate for this purpose; however, its calculation is itself highly demanding. Here, we introduce a fast and accurate algorithm for committor estimation by leveraging highly parallelizable short trajectory simulations and analogue prediction. The resulting committor is represented via a neural network ansatz and subseque"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17753","kind":"arxiv","version":1},"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/2605.17753/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":"2605.17753","created_at":"2026-05-20T00:04:56.505021+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.17753v1","created_at":"2026-05-20T00:04:56.505021+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17753","created_at":"2026-05-20T00:04:56.505021+00:00"},{"alias_kind":"pith_short_12","alias_value":"3DFXZYXXQ2ZW","created_at":"2026-05-20T00:04:56.505021+00:00"},{"alias_kind":"pith_short_16","alias_value":"3DFXZYXXQ2ZWNXSM","created_at":"2026-05-20T00:04:56.505021+00:00"},{"alias_kind":"pith_short_8","alias_value":"3DFXZYXX","created_at":"2026-05-20T00:04:56.505021+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/3DFXZYXXQ2ZWNXSM4BWC4VDTOE","json":"https://pith.science/pith/3DFXZYXXQ2ZWNXSM4BWC4VDTOE.json","graph_json":"https://pith.science/api/pith-number/3DFXZYXXQ2ZWNXSM4BWC4VDTOE/graph.json","events_json":"https://pith.science/api/pith-number/3DFXZYXXQ2ZWNXSM4BWC4VDTOE/events.json","paper":"https://pith.science/paper/3DFXZYXX"},"agent_actions":{"view_html":"https://pith.science/pith/3DFXZYXXQ2ZWNXSM4BWC4VDTOE","download_json":"https://pith.science/pith/3DFXZYXXQ2ZWNXSM4BWC4VDTOE.json","view_paper":"https://pith.science/paper/3DFXZYXX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.17753&json=true","fetch_graph":"https://pith.science/api/pith-number/3DFXZYXXQ2ZWNXSM4BWC4VDTOE/graph.json","fetch_events":"https://pith.science/api/pith-number/3DFXZYXXQ2ZWNXSM4BWC4VDTOE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3DFXZYXXQ2ZWNXSM4BWC4VDTOE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3DFXZYXXQ2ZWNXSM4BWC4VDTOE/action/storage_attestation","attest_author":"https://pith.science/pith/3DFXZYXXQ2ZWNXSM4BWC4VDTOE/action/author_attestation","sign_citation":"https://pith.science/pith/3DFXZYXXQ2ZWNXSM4BWC4VDTOE/action/citation_signature","submit_replication":"https://pith.science/pith/3DFXZYXXQ2ZWNXSM4BWC4VDTOE/action/replication_record"}},"created_at":"2026-05-20T00:04:56.505021+00:00","updated_at":"2026-05-20T00:04:56.505021+00:00"}