{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:2X2CG5KAJA2CJU3A2ABLQH75QN","short_pith_number":"pith:2X2CG5KA","schema_version":"1.0","canonical_sha256":"d5f4237540483424d360d002b81ffd836910cf5c014c3aee8af5f9ee4ca98801","source":{"kind":"arxiv","id":"1808.04526","version":2},"attestation_state":"computed","paper":{"title":"A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.chem-ph","authors_text":"Christopher Collins, David J. Yaron, Geoffrey J. Gordon, Haichen Li, Matteus Tanha","submitted_at":"2018-08-14T05:16:05Z","abstract_excerpt":"Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an integral part of the prediction process. This is done by implementing self-consistent-charge Density-Functional-Tight-Binding (DFTB) theory as a layer for use in deep learning models. The DFTB layer takes, as input, Hamiltonian matrix elements generated from earlier layers and produces, as output, electronic properties from self-consistent field solutions of the corresponding DFTB Hamiltonian. Backpropagation enable"},"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":"1808.04526","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2018-08-14T05:16:05Z","cross_cats_sorted":[],"title_canon_sha256":"0a5d88d49a62eaa612ade57bbde1d4e665be247e07b928c0a982ace0cc5f29de","abstract_canon_sha256":"6cc9ca4b8b1c718c793e89bcfbcbc98e75140c3947fc29b3e83e5d058c9c1b1c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:39.828156Z","signature_b64":"MFeMd+8k+mvCc8Ye77RjzJy1KGNWaW1TGESdWStLJWzj0M5FKRD7n1fsx8cZevNup5ay11Y3uUF7XQvft/+5BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d5f4237540483424d360d002b81ffd836910cf5c014c3aee8af5f9ee4ca98801","last_reissued_at":"2026-05-18T00:07:39.827488Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:39.827488Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.chem-ph","authors_text":"Christopher Collins, David J. Yaron, Geoffrey J. Gordon, Haichen Li, Matteus Tanha","submitted_at":"2018-08-14T05:16:05Z","abstract_excerpt":"Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an integral part of the prediction process. This is done by implementing self-consistent-charge Density-Functional-Tight-Binding (DFTB) theory as a layer for use in deep learning models. The DFTB layer takes, as input, Hamiltonian matrix elements generated from earlier layers and produces, as output, electronic properties from self-consistent field solutions of the corresponding DFTB Hamiltonian. Backpropagation enable"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04526","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":""},"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":"1808.04526","created_at":"2026-05-18T00:07:39.827592+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.04526v2","created_at":"2026-05-18T00:07:39.827592+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04526","created_at":"2026-05-18T00:07:39.827592+00:00"},{"alias_kind":"pith_short_12","alias_value":"2X2CG5KAJA2C","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"2X2CG5KAJA2CJU3A","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"2X2CG5KA","created_at":"2026-05-18T12:32:02.567920+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/2X2CG5KAJA2CJU3A2ABLQH75QN","json":"https://pith.science/pith/2X2CG5KAJA2CJU3A2ABLQH75QN.json","graph_json":"https://pith.science/api/pith-number/2X2CG5KAJA2CJU3A2ABLQH75QN/graph.json","events_json":"https://pith.science/api/pith-number/2X2CG5KAJA2CJU3A2ABLQH75QN/events.json","paper":"https://pith.science/paper/2X2CG5KA"},"agent_actions":{"view_html":"https://pith.science/pith/2X2CG5KAJA2CJU3A2ABLQH75QN","download_json":"https://pith.science/pith/2X2CG5KAJA2CJU3A2ABLQH75QN.json","view_paper":"https://pith.science/paper/2X2CG5KA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.04526&json=true","fetch_graph":"https://pith.science/api/pith-number/2X2CG5KAJA2CJU3A2ABLQH75QN/graph.json","fetch_events":"https://pith.science/api/pith-number/2X2CG5KAJA2CJU3A2ABLQH75QN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2X2CG5KAJA2CJU3A2ABLQH75QN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2X2CG5KAJA2CJU3A2ABLQH75QN/action/storage_attestation","attest_author":"https://pith.science/pith/2X2CG5KAJA2CJU3A2ABLQH75QN/action/author_attestation","sign_citation":"https://pith.science/pith/2X2CG5KAJA2CJU3A2ABLQH75QN/action/citation_signature","submit_replication":"https://pith.science/pith/2X2CG5KAJA2CJU3A2ABLQH75QN/action/replication_record"}},"created_at":"2026-05-18T00:07:39.827592+00:00","updated_at":"2026-05-18T00:07:39.827592+00:00"}