{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:EP672L5ZOZ7RRXGHVWZF5TH6CS","short_pith_number":"pith:EP672L5Z","schema_version":"1.0","canonical_sha256":"23fdfd2fb9767f18dcc7adb25eccfe1481703efd54c0d23b65e4e5e5f9840066","source":{"kind":"arxiv","id":"1908.06714","version":2},"attestation_state":"computed","paper":{"title":"Machine learning the computational cost of quantum chemistry","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"Guido Falk von Rudorff, Max Schwilk, O. Anatole von Lilienfeld, Stefan Heinen","submitted_at":"2019-08-19T11:54:29Z","abstract_excerpt":"Computational quantum mechanics based molecular and materials design campaigns consume increasingly more high-performance compute resources, making improved job scheduling efficiency desirable in order to reduce carbon footprint or wasteful spending. We introduce quantum machine learning (QML) models of the computational cost of common quantum chemistry tasks. For 2D non-linear toy systems, single point, geometry optimization, and transition state calculations the out of sample prediction error of QML models of wall times decays systematically with training set size. We present numerical evide"},"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":"1908.06714","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2019-08-19T11:54:29Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"030a3ca2089be231a7e83f9d8f0883476d8da834460ccc6f598798ad608774ce","abstract_canon_sha256":"9b0d21289eaaf817a66dd4cb2d03bf82d2d75cfabbfe96121b7853c56afe851f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:09:38.966111Z","signature_b64":"imuXypmyMdXvCGBYHjFhIJR0DOzsl4sf3xWlIr0hr1i9B1hOzQeZD3P6SGtdqG8HagalKBLZudR+5bcDcYVJBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23fdfd2fb9767f18dcc7adb25eccfe1481703efd54c0d23b65e4e5e5f9840066","last_reissued_at":"2026-07-05T01:09:38.965640Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:09:38.965640Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine learning the computational cost of quantum chemistry","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"Guido Falk von Rudorff, Max Schwilk, O. Anatole von Lilienfeld, Stefan Heinen","submitted_at":"2019-08-19T11:54:29Z","abstract_excerpt":"Computational quantum mechanics based molecular and materials design campaigns consume increasingly more high-performance compute resources, making improved job scheduling efficiency desirable in order to reduce carbon footprint or wasteful spending. We introduce quantum machine learning (QML) models of the computational cost of common quantum chemistry tasks. For 2D non-linear toy systems, single point, geometry optimization, and transition state calculations the out of sample prediction error of QML models of wall times decays systematically with training set size. We present numerical evide"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06714","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/1908.06714/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":"1908.06714","created_at":"2026-07-05T01:09:38.965704+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.06714v2","created_at":"2026-07-05T01:09:38.965704+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06714","created_at":"2026-07-05T01:09:38.965704+00:00"},{"alias_kind":"pith_short_12","alias_value":"EP672L5ZOZ7R","created_at":"2026-07-05T01:09:38.965704+00:00"},{"alias_kind":"pith_short_16","alias_value":"EP672L5ZOZ7RRXGH","created_at":"2026-07-05T01:09:38.965704+00:00"},{"alias_kind":"pith_short_8","alias_value":"EP672L5Z","created_at":"2026-07-05T01:09:38.965704+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.20609","citing_title":"Representative Random Sampling of Chemical Space","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EP672L5ZOZ7RRXGHVWZF5TH6CS","json":"https://pith.science/pith/EP672L5ZOZ7RRXGHVWZF5TH6CS.json","graph_json":"https://pith.science/api/pith-number/EP672L5ZOZ7RRXGHVWZF5TH6CS/graph.json","events_json":"https://pith.science/api/pith-number/EP672L5ZOZ7RRXGHVWZF5TH6CS/events.json","paper":"https://pith.science/paper/EP672L5Z"},"agent_actions":{"view_html":"https://pith.science/pith/EP672L5ZOZ7RRXGHVWZF5TH6CS","download_json":"https://pith.science/pith/EP672L5ZOZ7RRXGHVWZF5TH6CS.json","view_paper":"https://pith.science/paper/EP672L5Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.06714&json=true","fetch_graph":"https://pith.science/api/pith-number/EP672L5ZOZ7RRXGHVWZF5TH6CS/graph.json","fetch_events":"https://pith.science/api/pith-number/EP672L5ZOZ7RRXGHVWZF5TH6CS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EP672L5ZOZ7RRXGHVWZF5TH6CS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EP672L5ZOZ7RRXGHVWZF5TH6CS/action/storage_attestation","attest_author":"https://pith.science/pith/EP672L5ZOZ7RRXGHVWZF5TH6CS/action/author_attestation","sign_citation":"https://pith.science/pith/EP672L5ZOZ7RRXGHVWZF5TH6CS/action/citation_signature","submit_replication":"https://pith.science/pith/EP672L5ZOZ7RRXGHVWZF5TH6CS/action/replication_record"}},"created_at":"2026-07-05T01:09:38.965704+00:00","updated_at":"2026-07-05T01:09:38.965704+00:00"}