{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:OZMAHDSYKKCFGEK4JTA3KVKR3L","short_pith_number":"pith:OZMAHDSY","canonical_record":{"source":{"id":"2309.15127","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2023-09-23T00:25:06Z","cross_cats_sorted":["cond-mat.mtrl-sci","cs.LG","quant-ph"],"title_canon_sha256":"a750f5e7f637eb7068fb9bf54d811f53a8c1ae78df998461e5dc53c61246d84e","abstract_canon_sha256":"fc9adbbef9ce00caddd5c5b406cf9a9e011127b04857a509ddf6b418fed65a21"},"schema_version":"1.0"},"canonical_sha256":"7658038e58528453115c4cc1b55551dacf263940a2024e16ddec955237a90211","source":{"kind":"arxiv","id":"2309.15127","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.15127","created_at":"2026-07-05T07:58:23Z"},{"alias_kind":"arxiv_version","alias_value":"2309.15127v2","created_at":"2026-07-05T07:58:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.15127","created_at":"2026-07-05T07:58:23Z"},{"alias_kind":"pith_short_12","alias_value":"OZMAHDSYKKCF","created_at":"2026-07-05T07:58:23Z"},{"alias_kind":"pith_short_16","alias_value":"OZMAHDSYKKCFGEK4","created_at":"2026-07-05T07:58:23Z"},{"alias_kind":"pith_short_8","alias_value":"OZMAHDSY","created_at":"2026-07-05T07:58:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:OZMAHDSYKKCFGEK4JTA3KVKR3L","target":"record","payload":{"canonical_record":{"source":{"id":"2309.15127","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2023-09-23T00:25:06Z","cross_cats_sorted":["cond-mat.mtrl-sci","cs.LG","quant-ph"],"title_canon_sha256":"a750f5e7f637eb7068fb9bf54d811f53a8c1ae78df998461e5dc53c61246d84e","abstract_canon_sha256":"fc9adbbef9ce00caddd5c5b406cf9a9e011127b04857a509ddf6b418fed65a21"},"schema_version":"1.0"},"canonical_sha256":"7658038e58528453115c4cc1b55551dacf263940a2024e16ddec955237a90211","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:58:23.567445Z","signature_b64":"uLNSStqB1pDgFrP+tHQNinaX2Zjfr0JNp35IfztuayIvg14luVjm42zgpOT3POQWcQrlnFRGzflVkuUaTtvABg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7658038e58528453115c4cc1b55551dacf263940a2024e16ddec955237a90211","last_reissued_at":"2026-07-05T07:58:23.566963Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:58:23.566963Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.15127","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:58:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LMdGMAxdSj2RADgSgc5iKapEEdHppO3g5BH9x6De/02B8ikOYxpAeqgi7ep5KE+JBjRUVu4jlXf5tRqii6bVDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:07:47.627244Z"},"content_sha256":"4fe74f493f0aec82e8bb662401e61835354a0552790756af15fb5560c18cff8a","schema_version":"1.0","event_id":"sha256:4fe74f493f0aec82e8bb662401e61835354a0552790756af15fb5560c18cff8a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:OZMAHDSYKKCFGEK4JTA3KVKR3L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Grad DFT: a software library for machine learning enhanced density functional theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","cs.LG","quant-ph"],"primary_cat":"physics.chem-ph","authors_text":"Jack S. Baker, Juan Miguel Arrazola, Matija Medvidovic, Pablo A. M. Casares, Roberto dos Reis","submitted_at":"2023-09-23T00:25:06Z","abstract_excerpt":"Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT; an endeavor with many open questions and technical challenges. In this work, we present Grad DFT: a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.15127","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/2309.15127/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:58:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1mNYHTWFmYXQWtIlktSAZI8tcDbe8eW4fmVWpj4rrEP2w+f2V99gFLdymkrXzjZ5BEnLb8nu1bQZUZTXpCU/CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:07:47.628167Z"},"content_sha256":"4150a87a5bb90d349e8266a8ede18014ae7b9b2f9bd53fb2be5e1aa227d5611f","schema_version":"1.0","event_id":"sha256:4150a87a5bb90d349e8266a8ede18014ae7b9b2f9bd53fb2be5e1aa227d5611f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OZMAHDSYKKCFGEK4JTA3KVKR3L/bundle.json","state_url":"https://pith.science/pith/OZMAHDSYKKCFGEK4JTA3KVKR3L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OZMAHDSYKKCFGEK4JTA3KVKR3L/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T16:07:47Z","links":{"resolver":"https://pith.science/pith/OZMAHDSYKKCFGEK4JTA3KVKR3L","bundle":"https://pith.science/pith/OZMAHDSYKKCFGEK4JTA3KVKR3L/bundle.json","state":"https://pith.science/pith/OZMAHDSYKKCFGEK4JTA3KVKR3L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OZMAHDSYKKCFGEK4JTA3KVKR3L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:OZMAHDSYKKCFGEK4JTA3KVKR3L","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"fc9adbbef9ce00caddd5c5b406cf9a9e011127b04857a509ddf6b418fed65a21","cross_cats_sorted":["cond-mat.mtrl-sci","cs.LG","quant-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2023-09-23T00:25:06Z","title_canon_sha256":"a750f5e7f637eb7068fb9bf54d811f53a8c1ae78df998461e5dc53c61246d84e"},"schema_version":"1.0","source":{"id":"2309.15127","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.15127","created_at":"2026-07-05T07:58:23Z"},{"alias_kind":"arxiv_version","alias_value":"2309.15127v2","created_at":"2026-07-05T07:58:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.15127","created_at":"2026-07-05T07:58:23Z"},{"alias_kind":"pith_short_12","alias_value":"OZMAHDSYKKCF","created_at":"2026-07-05T07:58:23Z"},{"alias_kind":"pith_short_16","alias_value":"OZMAHDSYKKCFGEK4","created_at":"2026-07-05T07:58:23Z"},{"alias_kind":"pith_short_8","alias_value":"OZMAHDSY","created_at":"2026-07-05T07:58:23Z"}],"graph_snapshots":[{"event_id":"sha256:4150a87a5bb90d349e8266a8ede18014ae7b9b2f9bd53fb2be5e1aa227d5611f","target":"graph","created_at":"2026-07-05T07:58:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2309.15127/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT; an endeavor with many open questions and technical challenges. In this work, we present Grad DFT: a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machin","authors_text":"Jack S. Baker, Juan Miguel Arrazola, Matija Medvidovic, Pablo A. M. Casares, Roberto dos Reis","cross_cats":["cond-mat.mtrl-sci","cs.LG","quant-ph"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2023-09-23T00:25:06Z","title":"Grad DFT: a software library for machine learning enhanced density functional theory"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.15127","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4fe74f493f0aec82e8bb662401e61835354a0552790756af15fb5560c18cff8a","target":"record","created_at":"2026-07-05T07:58:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"fc9adbbef9ce00caddd5c5b406cf9a9e011127b04857a509ddf6b418fed65a21","cross_cats_sorted":["cond-mat.mtrl-sci","cs.LG","quant-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2023-09-23T00:25:06Z","title_canon_sha256":"a750f5e7f637eb7068fb9bf54d811f53a8c1ae78df998461e5dc53c61246d84e"},"schema_version":"1.0","source":{"id":"2309.15127","kind":"arxiv","version":2}},"canonical_sha256":"7658038e58528453115c4cc1b55551dacf263940a2024e16ddec955237a90211","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7658038e58528453115c4cc1b55551dacf263940a2024e16ddec955237a90211","first_computed_at":"2026-07-05T07:58:23.566963Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:58:23.566963Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uLNSStqB1pDgFrP+tHQNinaX2Zjfr0JNp35IfztuayIvg14luVjm42zgpOT3POQWcQrlnFRGzflVkuUaTtvABg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:58:23.567445Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.15127","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4fe74f493f0aec82e8bb662401e61835354a0552790756af15fb5560c18cff8a","sha256:4150a87a5bb90d349e8266a8ede18014ae7b9b2f9bd53fb2be5e1aa227d5611f"],"state_sha256":"9d0beea45a570ebe1a7139a0647c09c9e805de7307dc1bdeb7f2514eec7bd4e3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8cBpW72+vyy87sGCx91qkYBsPHc6LIUi1usSD0n2UkVU2CnO4MtcqkMcZ/nZf4s7luU2MnG0soeztqZz+XybCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T16:07:47.636281Z","bundle_sha256":"c57134b56192f73aca6497f602ab84012a5526457de469280ba6991892f16fa9"}}