{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CKGVNAAHBLIPSWCZTKYJXSNXEG","short_pith_number":"pith:CKGVNAAH","schema_version":"1.0","canonical_sha256":"128d5680070ad0f958599ab09bc9b721848fa110b6b047dfe807ccbc994e42b4","source":{"kind":"arxiv","id":"2411.03698","version":1},"attestation_state":"computed","paper":{"title":"High-Dimensional Operator Learning for Molecular Density Functional Theory","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.chem-ph","authors_text":"Jianzhong Wu, Jikai Sun, Jinni Yang, Runtong Pan","submitted_at":"2024-11-06T06:41:27Z","abstract_excerpt":"Classical density functional theory (cDFT) provides a systematic approach to predict the structure and thermodynamic properties of chemical systems through the single-molecule density profiles. Whereas the statistical-mechanical framework is theoretically rigorous, its practical applications are often constrained by challenges in formulating a reliable free-energy functional and the complexity of solving multidimensional integro-differential equations. In this work, we established an optimized operator learning method that effectively separates the high-dimensional molecular density profile in"},"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":"2411.03698","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2024-11-06T06:41:27Z","cross_cats_sorted":[],"title_canon_sha256":"e5af780928d2841f9e8dc352f7f71f8487fd5f38bc098e5a5ec6d3f5d59cd2c0","abstract_canon_sha256":"5f6c503a14b0f25dd2406ab890a3ab5018ec0608ec444a10fec6042b89468830"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:31:54.535408Z","signature_b64":"YbzsqQp/4Fq9zkJqQOx+rBInrSFap/Sxnx1n3gts7MSTeLFtmgYcZg5lq3DPwc3UwW2SDTNUIF07hn2y51tSCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"128d5680070ad0f958599ab09bc9b721848fa110b6b047dfe807ccbc994e42b4","last_reissued_at":"2026-07-05T09:31:54.534857Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:31:54.534857Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"High-Dimensional Operator Learning for Molecular Density Functional Theory","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.chem-ph","authors_text":"Jianzhong Wu, Jikai Sun, Jinni Yang, Runtong Pan","submitted_at":"2024-11-06T06:41:27Z","abstract_excerpt":"Classical density functional theory (cDFT) provides a systematic approach to predict the structure and thermodynamic properties of chemical systems through the single-molecule density profiles. Whereas the statistical-mechanical framework is theoretically rigorous, its practical applications are often constrained by challenges in formulating a reliable free-energy functional and the complexity of solving multidimensional integro-differential equations. In this work, we established an optimized operator learning method that effectively separates the high-dimensional molecular density profile in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.03698","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/2411.03698/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":"2411.03698","created_at":"2026-07-05T09:31:54.534917+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.03698v1","created_at":"2026-07-05T09:31:54.534917+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.03698","created_at":"2026-07-05T09:31:54.534917+00:00"},{"alias_kind":"pith_short_12","alias_value":"CKGVNAAHBLIP","created_at":"2026-07-05T09:31:54.534917+00:00"},{"alias_kind":"pith_short_16","alias_value":"CKGVNAAHBLIPSWCZ","created_at":"2026-07-05T09:31:54.534917+00:00"},{"alias_kind":"pith_short_8","alias_value":"CKGVNAAH","created_at":"2026-07-05T09:31:54.534917+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2504.17599","citing_title":"Dynamical gauge invariance of statistical mechanics","ref_index":84,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CKGVNAAHBLIPSWCZTKYJXSNXEG","json":"https://pith.science/pith/CKGVNAAHBLIPSWCZTKYJXSNXEG.json","graph_json":"https://pith.science/api/pith-number/CKGVNAAHBLIPSWCZTKYJXSNXEG/graph.json","events_json":"https://pith.science/api/pith-number/CKGVNAAHBLIPSWCZTKYJXSNXEG/events.json","paper":"https://pith.science/paper/CKGVNAAH"},"agent_actions":{"view_html":"https://pith.science/pith/CKGVNAAHBLIPSWCZTKYJXSNXEG","download_json":"https://pith.science/pith/CKGVNAAHBLIPSWCZTKYJXSNXEG.json","view_paper":"https://pith.science/paper/CKGVNAAH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.03698&json=true","fetch_graph":"https://pith.science/api/pith-number/CKGVNAAHBLIPSWCZTKYJXSNXEG/graph.json","fetch_events":"https://pith.science/api/pith-number/CKGVNAAHBLIPSWCZTKYJXSNXEG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CKGVNAAHBLIPSWCZTKYJXSNXEG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CKGVNAAHBLIPSWCZTKYJXSNXEG/action/storage_attestation","attest_author":"https://pith.science/pith/CKGVNAAHBLIPSWCZTKYJXSNXEG/action/author_attestation","sign_citation":"https://pith.science/pith/CKGVNAAHBLIPSWCZTKYJXSNXEG/action/citation_signature","submit_replication":"https://pith.science/pith/CKGVNAAHBLIPSWCZTKYJXSNXEG/action/replication_record"}},"created_at":"2026-07-05T09:31:54.534917+00:00","updated_at":"2026-07-05T09:31:54.534917+00:00"}