{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZYVQSSH2IAOFSKDSN3QJTAKUF2","short_pith_number":"pith:ZYVQSSH2","schema_version":"1.0","canonical_sha256":"ce2b0948fa401c5928726ee09981542ea37144e906b1c69b599e9f4afa8bc7fd","source":{"kind":"arxiv","id":"2507.16531","version":1},"attestation_state":"computed","paper":{"title":"Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.soft","authors_text":"Debarchan Basu, Sandeep Kumar, Soumya Mondal, Subhanu Halder, Tarak Karmakar","submitted_at":"2025-07-22T12:39:30Z","abstract_excerpt":"Coarse-grained (CG) molecular dynamics (MD) simulations can simulate large molecular complexes over extended timescales by reducing degrees of freedom. A critical step in CG modeling is the selection of the CG mapping algorithm, which directly influences both accuracy and interpretability of the model. Despite progress, the optimal strategy for coarse-graining remains a challenging task, highlighting the necessity for a comprehensive theoretical framework. In this work, we present a graph-based coarsening approach to develop CG models. Coarse-grained sites are obtained through edge contraction"},"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":"2507.16531","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cond-mat.soft","submitted_at":"2025-07-22T12:39:30Z","cross_cats_sorted":[],"title_canon_sha256":"4d9d0b7a5c9b842d9328ab0627104b5c9a20082b85043274da52fbba9c22757f","abstract_canon_sha256":"ca2369e9ffb595914a1f18553b0e53a3e5798844b1d1be867be6bfd041d32efb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:41:11.555686Z","signature_b64":"Vhbr3iUaW7bp9McCuGiPNxQdC17h0mfpI/ROoTR9/H3K2I2rBgAqkVWpKqfqrjn1l+x3csFvgJlyXuNsXw1ZAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce2b0948fa401c5928726ee09981542ea37144e906b1c69b599e9f4afa8bc7fd","last_reissued_at":"2026-07-05T11:41:11.555148Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:41:11.555148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.soft","authors_text":"Debarchan Basu, Sandeep Kumar, Soumya Mondal, Subhanu Halder, Tarak Karmakar","submitted_at":"2025-07-22T12:39:30Z","abstract_excerpt":"Coarse-grained (CG) molecular dynamics (MD) simulations can simulate large molecular complexes over extended timescales by reducing degrees of freedom. A critical step in CG modeling is the selection of the CG mapping algorithm, which directly influences both accuracy and interpretability of the model. Despite progress, the optimal strategy for coarse-graining remains a challenging task, highlighting the necessity for a comprehensive theoretical framework. In this work, we present a graph-based coarsening approach to develop CG models. Coarse-grained sites are obtained through edge contraction"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.16531","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/2507.16531/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":"2507.16531","created_at":"2026-07-05T11:41:11.555216+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.16531v1","created_at":"2026-07-05T11:41:11.555216+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.16531","created_at":"2026-07-05T11:41:11.555216+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZYVQSSH2IAOF","created_at":"2026-07-05T11:41:11.555216+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZYVQSSH2IAOFSKDS","created_at":"2026-07-05T11:41:11.555216+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZYVQSSH2","created_at":"2026-07-05T11:41:11.555216+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/ZYVQSSH2IAOFSKDSN3QJTAKUF2","json":"https://pith.science/pith/ZYVQSSH2IAOFSKDSN3QJTAKUF2.json","graph_json":"https://pith.science/api/pith-number/ZYVQSSH2IAOFSKDSN3QJTAKUF2/graph.json","events_json":"https://pith.science/api/pith-number/ZYVQSSH2IAOFSKDSN3QJTAKUF2/events.json","paper":"https://pith.science/paper/ZYVQSSH2"},"agent_actions":{"view_html":"https://pith.science/pith/ZYVQSSH2IAOFSKDSN3QJTAKUF2","download_json":"https://pith.science/pith/ZYVQSSH2IAOFSKDSN3QJTAKUF2.json","view_paper":"https://pith.science/paper/ZYVQSSH2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.16531&json=true","fetch_graph":"https://pith.science/api/pith-number/ZYVQSSH2IAOFSKDSN3QJTAKUF2/graph.json","fetch_events":"https://pith.science/api/pith-number/ZYVQSSH2IAOFSKDSN3QJTAKUF2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZYVQSSH2IAOFSKDSN3QJTAKUF2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZYVQSSH2IAOFSKDSN3QJTAKUF2/action/storage_attestation","attest_author":"https://pith.science/pith/ZYVQSSH2IAOFSKDSN3QJTAKUF2/action/author_attestation","sign_citation":"https://pith.science/pith/ZYVQSSH2IAOFSKDSN3QJTAKUF2/action/citation_signature","submit_replication":"https://pith.science/pith/ZYVQSSH2IAOFSKDSN3QJTAKUF2/action/replication_record"}},"created_at":"2026-07-05T11:41:11.555216+00:00","updated_at":"2026-07-05T11:41:11.555216+00:00"}