{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:A7BFXJ7CJPOIGDO6NJNY2FLASG","short_pith_number":"pith:A7BFXJ7C","schema_version":"1.0","canonical_sha256":"07c25ba7e24bdc830dde6a5b8d1560919fc031086dc8f3c9890bd49379d5ebd9","source":{"kind":"arxiv","id":"2211.13998","version":2},"attestation_state":"computed","paper":{"title":"Deep-neural-network approach to solving the ab initio nuclear structure problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","quant-ph"],"primary_cat":"nucl-th","authors_text":"Pengwei Zhao, Yilong Yang","submitted_at":"2022-11-25T10:14:04Z","abstract_excerpt":"Predicting the structure of quantum many-body systems from the first principles of quantum mechanics is a common challenge in physics, chemistry, and material science. Deep machine learning has proven to be a powerful tool for solving condensed matter and chemistry problems, while for atomic nuclei it is still quite challenging because of the complicated nucleon-nucleon interactions, which strongly couple the spatial, spin, and isospin degrees of freedom. By combining essential physics of the nuclear wave functions and the strong expressive power of artificial neural networks, we develop Feynm"},"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":"2211.13998","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"nucl-th","submitted_at":"2022-11-25T10:14:04Z","cross_cats_sorted":["cond-mat.dis-nn","quant-ph"],"title_canon_sha256":"43ce958f641ebaa242615a6224a48d8d85c1451891b1a789ff5c725facbf533b","abstract_canon_sha256":"7b324eaefede3b416815d041626496759fc0dc49d7194816186d482279461700"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:57:34.500798Z","signature_b64":"OUHH/6ATHWI6PS8PHlBUfATf8vfT3DxmJWMb9Ejnz/aiqO6EquBKTVPjS+Fm4kvnRrJ0FnjKgM30TlvK00BlAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07c25ba7e24bdc830dde6a5b8d1560919fc031086dc8f3c9890bd49379d5ebd9","last_reissued_at":"2026-07-05T05:57:34.500368Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:57:34.500368Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep-neural-network approach to solving the ab initio nuclear structure problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","quant-ph"],"primary_cat":"nucl-th","authors_text":"Pengwei Zhao, Yilong Yang","submitted_at":"2022-11-25T10:14:04Z","abstract_excerpt":"Predicting the structure of quantum many-body systems from the first principles of quantum mechanics is a common challenge in physics, chemistry, and material science. Deep machine learning has proven to be a powerful tool for solving condensed matter and chemistry problems, while for atomic nuclei it is still quite challenging because of the complicated nucleon-nucleon interactions, which strongly couple the spatial, spin, and isospin degrees of freedom. By combining essential physics of the nuclear wave functions and the strong expressive power of artificial neural networks, we develop Feynm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.13998","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2211.13998/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":"2211.13998","created_at":"2026-07-05T05:57:34.500422+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.13998v2","created_at":"2026-07-05T05:57:34.500422+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.13998","created_at":"2026-07-05T05:57:34.500422+00:00"},{"alias_kind":"pith_short_12","alias_value":"A7BFXJ7CJPOI","created_at":"2026-07-05T05:57:34.500422+00:00"},{"alias_kind":"pith_short_16","alias_value":"A7BFXJ7CJPOIGDO6","created_at":"2026-07-05T05:57:34.500422+00:00"},{"alias_kind":"pith_short_8","alias_value":"A7BFXJ7C","created_at":"2026-07-05T05:57:34.500422+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24380","citing_title":"Fully-heavy multiquarks in neural-network quantum states","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2606.09254","citing_title":"Meson-Nucleus Bound States with Neural-Network Quantum States","ref_index":45,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A7BFXJ7CJPOIGDO6NJNY2FLASG","json":"https://pith.science/pith/A7BFXJ7CJPOIGDO6NJNY2FLASG.json","graph_json":"https://pith.science/api/pith-number/A7BFXJ7CJPOIGDO6NJNY2FLASG/graph.json","events_json":"https://pith.science/api/pith-number/A7BFXJ7CJPOIGDO6NJNY2FLASG/events.json","paper":"https://pith.science/paper/A7BFXJ7C"},"agent_actions":{"view_html":"https://pith.science/pith/A7BFXJ7CJPOIGDO6NJNY2FLASG","download_json":"https://pith.science/pith/A7BFXJ7CJPOIGDO6NJNY2FLASG.json","view_paper":"https://pith.science/paper/A7BFXJ7C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.13998&json=true","fetch_graph":"https://pith.science/api/pith-number/A7BFXJ7CJPOIGDO6NJNY2FLASG/graph.json","fetch_events":"https://pith.science/api/pith-number/A7BFXJ7CJPOIGDO6NJNY2FLASG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A7BFXJ7CJPOIGDO6NJNY2FLASG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A7BFXJ7CJPOIGDO6NJNY2FLASG/action/storage_attestation","attest_author":"https://pith.science/pith/A7BFXJ7CJPOIGDO6NJNY2FLASG/action/author_attestation","sign_citation":"https://pith.science/pith/A7BFXJ7CJPOIGDO6NJNY2FLASG/action/citation_signature","submit_replication":"https://pith.science/pith/A7BFXJ7CJPOIGDO6NJNY2FLASG/action/replication_record"}},"created_at":"2026-07-05T05:57:34.500422+00:00","updated_at":"2026-07-05T05:57:34.500422+00:00"}