{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:XZKS7ES2DXXKNTMEVM2YBH6AKZ","short_pith_number":"pith:XZKS7ES2","schema_version":"1.0","canonical_sha256":"be552f925a1deea6cd84ab35809fc05669876d40547af55b3bb20878523d1531","source":{"kind":"arxiv","id":"1908.06198","version":2},"attestation_state":"computed","paper":{"title":"Machine Learning the Physical Non-Local Exchange-Correlation Functional of Density-Functional Theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.str-el","physics.chem-ph"],"primary_cat":"physics.comp-ph","authors_text":"Carlos L. Benavides-Riveros, Jonathan Schmidt, Miguel A. L. Marques","submitted_at":"2019-08-16T22:39:44Z","abstract_excerpt":"We train a neural network as the universal exchange-correlation functional of density-functional theory that simultaneously reproduces both the exact exchange-correlation energy and potential. This functional is extremely non-local, but retains the computational scaling of traditional local or semi-local approximations. It therefore holds the promise of solving some of the delocalization problems that plague density-functional theory, while maintaining the computational efficiency that characterizes the Kohn-Sham equations. Furthermore, by using automatic differentiation, a capability present "},"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":"1908.06198","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2019-08-16T22:39:44Z","cross_cats_sorted":["cond-mat.str-el","physics.chem-ph"],"title_canon_sha256":"a61eb65c3dd1352c9b62eea550f14cc5b8ca9a5b30bd322ae94116c5cb219cbd","abstract_canon_sha256":"d370efc98c59b1e0bce26e656c5266899e6d261e36a7839b9143e7950468496e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:10:54.266470Z","signature_b64":"IpndVIX+pfp2LsJ8pa6cDBgw0Co8bUQjJLZIRdDPrFdF5IHWNTwIcaHSDX78NwKWFT03VZakRNDHQjONmlxxAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be552f925a1deea6cd84ab35809fc05669876d40547af55b3bb20878523d1531","last_reissued_at":"2026-07-05T00:10:54.265916Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:10:54.265916Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine Learning the Physical Non-Local Exchange-Correlation Functional of Density-Functional Theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.str-el","physics.chem-ph"],"primary_cat":"physics.comp-ph","authors_text":"Carlos L. Benavides-Riveros, Jonathan Schmidt, Miguel A. L. Marques","submitted_at":"2019-08-16T22:39:44Z","abstract_excerpt":"We train a neural network as the universal exchange-correlation functional of density-functional theory that simultaneously reproduces both the exact exchange-correlation energy and potential. This functional is extremely non-local, but retains the computational scaling of traditional local or semi-local approximations. It therefore holds the promise of solving some of the delocalization problems that plague density-functional theory, while maintaining the computational efficiency that characterizes the Kohn-Sham equations. Furthermore, by using automatic differentiation, a capability present "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06198","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/1908.06198/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":"1908.06198","created_at":"2026-07-05T00:10:54.265975+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.06198v2","created_at":"2026-07-05T00:10:54.265975+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06198","created_at":"2026-07-05T00:10:54.265975+00:00"},{"alias_kind":"pith_short_12","alias_value":"XZKS7ES2DXXK","created_at":"2026-07-05T00:10:54.265975+00:00"},{"alias_kind":"pith_short_16","alias_value":"XZKS7ES2DXXKNTME","created_at":"2026-07-05T00:10:54.265975+00:00"},{"alias_kind":"pith_short_8","alias_value":"XZKS7ES2","created_at":"2026-07-05T00:10:54.265975+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/XZKS7ES2DXXKNTMEVM2YBH6AKZ","json":"https://pith.science/pith/XZKS7ES2DXXKNTMEVM2YBH6AKZ.json","graph_json":"https://pith.science/api/pith-number/XZKS7ES2DXXKNTMEVM2YBH6AKZ/graph.json","events_json":"https://pith.science/api/pith-number/XZKS7ES2DXXKNTMEVM2YBH6AKZ/events.json","paper":"https://pith.science/paper/XZKS7ES2"},"agent_actions":{"view_html":"https://pith.science/pith/XZKS7ES2DXXKNTMEVM2YBH6AKZ","download_json":"https://pith.science/pith/XZKS7ES2DXXKNTMEVM2YBH6AKZ.json","view_paper":"https://pith.science/paper/XZKS7ES2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.06198&json=true","fetch_graph":"https://pith.science/api/pith-number/XZKS7ES2DXXKNTMEVM2YBH6AKZ/graph.json","fetch_events":"https://pith.science/api/pith-number/XZKS7ES2DXXKNTMEVM2YBH6AKZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XZKS7ES2DXXKNTMEVM2YBH6AKZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XZKS7ES2DXXKNTMEVM2YBH6AKZ/action/storage_attestation","attest_author":"https://pith.science/pith/XZKS7ES2DXXKNTMEVM2YBH6AKZ/action/author_attestation","sign_citation":"https://pith.science/pith/XZKS7ES2DXXKNTMEVM2YBH6AKZ/action/citation_signature","submit_replication":"https://pith.science/pith/XZKS7ES2DXXKNTMEVM2YBH6AKZ/action/replication_record"}},"created_at":"2026-07-05T00:10:54.265975+00:00","updated_at":"2026-07-05T00:10:54.265975+00:00"}