{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:7BV4LA6NZF3SJCUV7NS4IALVXZ","short_pith_number":"pith:7BV4LA6N","schema_version":"1.0","canonical_sha256":"f86bc583cdc977248a95fb65c40175be6c8f66de722b9792984d6c5a8dc71289","source":{"kind":"arxiv","id":"2607.04095","version":1},"attestation_state":"computed","paper":{"title":"ML and AI for density functional theory: different priorities for Kohn-Sham and orbital-free DFT, for electronic and nuclear DFT","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","nucl-th","quant-ph"],"primary_cat":"physics.chem-ph","authors_text":"Sergei Manzhos, Xin-Hui Wu","submitted_at":"2026-07-05T03:16:48Z","abstract_excerpt":"We overview similarities and, importantly, differences in computational bottlenecks and accuracy requirements that can be addressed with machine learning (ML) and artificial intelligence (AI) techniques in electronic and nuclear DFT. From these follow different promising methodological and algorithmic choices depending on whether one machine learns the exchange correlation (XC) functional, the kinetic energy functional (KEF), the density or the basis functions. In particular, while the popular deep neural networks remain a potent choice in the context of KS DFT, we highlight their disadvantage"},"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":"2607.04095","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2026-07-05T03:16:48Z","cross_cats_sorted":["cond-mat.mtrl-sci","nucl-th","quant-ph"],"title_canon_sha256":"3dc133f4cceb8ebc8cfa3692f16e6a6cf4352e512a591df10f4d679aea518f8d","abstract_canon_sha256":"7cdc2f2f77961f9597393bced57e04ce1d0298b0c966f12ce493aa23daf4b405"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:18:57.123183Z","signature_b64":"QK7qvQ1GQ54/xWnTi6L+OaXdtM0lXgRCsJg9Np0kHeI9ccCB6xturjF8Kxa7kf6HTlRdLWXI2xgjIKq4y8rLAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f86bc583cdc977248a95fb65c40175be6c8f66de722b9792984d6c5a8dc71289","last_reissued_at":"2026-07-07T02:18:57.122438Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:18:57.122438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ML and AI for density functional theory: different priorities for Kohn-Sham and orbital-free DFT, for electronic and nuclear DFT","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","nucl-th","quant-ph"],"primary_cat":"physics.chem-ph","authors_text":"Sergei Manzhos, Xin-Hui Wu","submitted_at":"2026-07-05T03:16:48Z","abstract_excerpt":"We overview similarities and, importantly, differences in computational bottlenecks and accuracy requirements that can be addressed with machine learning (ML) and artificial intelligence (AI) techniques in electronic and nuclear DFT. From these follow different promising methodological and algorithmic choices depending on whether one machine learns the exchange correlation (XC) functional, the kinetic energy functional (KEF), the density or the basis functions. In particular, while the popular deep neural networks remain a potent choice in the context of KS DFT, we highlight their disadvantage"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.04095","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/2607.04095/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":"2607.04095","created_at":"2026-07-07T02:18:57.122555+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.04095v1","created_at":"2026-07-07T02:18:57.122555+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.04095","created_at":"2026-07-07T02:18:57.122555+00:00"},{"alias_kind":"pith_short_12","alias_value":"7BV4LA6NZF3S","created_at":"2026-07-07T02:18:57.122555+00:00"},{"alias_kind":"pith_short_16","alias_value":"7BV4LA6NZF3SJCUV","created_at":"2026-07-07T02:18:57.122555+00:00"},{"alias_kind":"pith_short_8","alias_value":"7BV4LA6N","created_at":"2026-07-07T02:18:57.122555+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/7BV4LA6NZF3SJCUV7NS4IALVXZ","json":"https://pith.science/pith/7BV4LA6NZF3SJCUV7NS4IALVXZ.json","graph_json":"https://pith.science/api/pith-number/7BV4LA6NZF3SJCUV7NS4IALVXZ/graph.json","events_json":"https://pith.science/api/pith-number/7BV4LA6NZF3SJCUV7NS4IALVXZ/events.json","paper":"https://pith.science/paper/7BV4LA6N"},"agent_actions":{"view_html":"https://pith.science/pith/7BV4LA6NZF3SJCUV7NS4IALVXZ","download_json":"https://pith.science/pith/7BV4LA6NZF3SJCUV7NS4IALVXZ.json","view_paper":"https://pith.science/paper/7BV4LA6N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.04095&json=true","fetch_graph":"https://pith.science/api/pith-number/7BV4LA6NZF3SJCUV7NS4IALVXZ/graph.json","fetch_events":"https://pith.science/api/pith-number/7BV4LA6NZF3SJCUV7NS4IALVXZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7BV4LA6NZF3SJCUV7NS4IALVXZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7BV4LA6NZF3SJCUV7NS4IALVXZ/action/storage_attestation","attest_author":"https://pith.science/pith/7BV4LA6NZF3SJCUV7NS4IALVXZ/action/author_attestation","sign_citation":"https://pith.science/pith/7BV4LA6NZF3SJCUV7NS4IALVXZ/action/citation_signature","submit_replication":"https://pith.science/pith/7BV4LA6NZF3SJCUV7NS4IALVXZ/action/replication_record"}},"created_at":"2026-07-07T02:18:57.122555+00:00","updated_at":"2026-07-07T02:18:57.122555+00:00"}