{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:IRJ2AACB44HML5ZMY52ZFWG26D","short_pith_number":"pith:IRJ2AACB","schema_version":"1.0","canonical_sha256":"4453a00041e70ec5f72cc77592d8daf0c06e93912a40f4aa8a53838bc8728f96","source":{"kind":"arxiv","id":"1109.2618","version":1},"attestation_state":"computed","paper":{"title":"Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","cond-mat.mtrl-sci","stat.ML"],"primary_cat":"physics.chem-ph","authors_text":"Alexandre Tkatchenko, Klaus-Robert M\\\"uller, Matthias Rupp, O. Anatole von Lilienfeld","submitted_at":"2011-09-12T20:33:46Z","abstract_excerpt":"We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schr\\\"odinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross-validation over more than seven thousand small organic molecules yields a mean absolute error of ~10 kcal/mol. Applicability is demonstrated for the prediction of molecular "},"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":"1109.2618","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2011-09-12T20:33:46Z","cross_cats_sorted":["cond-mat.dis-nn","cond-mat.mtrl-sci","stat.ML"],"title_canon_sha256":"38db06db89b35c9ee065ef10e32bb6f798fc27ff101ec560b709c5423ea16810","abstract_canon_sha256":"8ba55ceeb639cf5bae02df5ca508a7e60c7776fa17176985a678531d333e3c2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:00:33.998418Z","signature_b64":"JLxfQLwnqTXB69eEAOCuZnUNZSBNAN8EL9VBTHAdXr0p8NWweGN0TA0sHu6ux5vCpIvsWHUNkEm49TXIAC28DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4453a00041e70ec5f72cc77592d8daf0c06e93912a40f4aa8a53838bc8728f96","last_reissued_at":"2026-05-18T02:00:33.997847Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:00:33.997847Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","cond-mat.mtrl-sci","stat.ML"],"primary_cat":"physics.chem-ph","authors_text":"Alexandre Tkatchenko, Klaus-Robert M\\\"uller, Matthias Rupp, O. Anatole von Lilienfeld","submitted_at":"2011-09-12T20:33:46Z","abstract_excerpt":"We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schr\\\"odinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross-validation over more than seven thousand small organic molecules yields a mean absolute error of ~10 kcal/mol. Applicability is demonstrated for the prediction of molecular "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1109.2618","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":""},"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":"1109.2618","created_at":"2026-05-18T02:00:33.997935+00:00"},{"alias_kind":"arxiv_version","alias_value":"1109.2618v1","created_at":"2026-05-18T02:00:33.997935+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1109.2618","created_at":"2026-05-18T02:00:33.997935+00:00"},{"alias_kind":"pith_short_12","alias_value":"IRJ2AACB44HM","created_at":"2026-05-18T12:26:32.869790+00:00"},{"alias_kind":"pith_short_16","alias_value":"IRJ2AACB44HML5ZM","created_at":"2026-05-18T12:26:32.869790+00:00"},{"alias_kind":"pith_short_8","alias_value":"IRJ2AACB","created_at":"2026-05-18T12:26:32.869790+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.03222","citing_title":"IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IRJ2AACB44HML5ZMY52ZFWG26D","json":"https://pith.science/pith/IRJ2AACB44HML5ZMY52ZFWG26D.json","graph_json":"https://pith.science/api/pith-number/IRJ2AACB44HML5ZMY52ZFWG26D/graph.json","events_json":"https://pith.science/api/pith-number/IRJ2AACB44HML5ZMY52ZFWG26D/events.json","paper":"https://pith.science/paper/IRJ2AACB"},"agent_actions":{"view_html":"https://pith.science/pith/IRJ2AACB44HML5ZMY52ZFWG26D","download_json":"https://pith.science/pith/IRJ2AACB44HML5ZMY52ZFWG26D.json","view_paper":"https://pith.science/paper/IRJ2AACB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1109.2618&json=true","fetch_graph":"https://pith.science/api/pith-number/IRJ2AACB44HML5ZMY52ZFWG26D/graph.json","fetch_events":"https://pith.science/api/pith-number/IRJ2AACB44HML5ZMY52ZFWG26D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IRJ2AACB44HML5ZMY52ZFWG26D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IRJ2AACB44HML5ZMY52ZFWG26D/action/storage_attestation","attest_author":"https://pith.science/pith/IRJ2AACB44HML5ZMY52ZFWG26D/action/author_attestation","sign_citation":"https://pith.science/pith/IRJ2AACB44HML5ZMY52ZFWG26D/action/citation_signature","submit_replication":"https://pith.science/pith/IRJ2AACB44HML5ZMY52ZFWG26D/action/replication_record"}},"created_at":"2026-05-18T02:00:33.997935+00:00","updated_at":"2026-05-18T02:00:33.997935+00:00"}