{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:2KCJIWVGZKQGZ645EV2UWMEUHD","short_pith_number":"pith:2KCJIWVG","schema_version":"1.0","canonical_sha256":"d284945aa6caa06cfb9d25754b309438cba80b044cf1073149460a23c79d914d","source":{"kind":"arxiv","id":"1511.07883","version":1},"attestation_state":"computed","paper":{"title":"Machine Learning Exciton Dynamics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"Al\\'an Aspuru-Guzik, Edward Pyzer-Knapp, Florian H\\\"ase, St\\'ephanie Valleau","submitted_at":"2015-11-24T21:01:01Z","abstract_excerpt":"Obtaining the exciton dynamics of large photosynthetic complexes by using mixed quantum mechanics/molecular mechanics (QM/MM) is computationally demanding. We propose a machine learning technique, multi-layer perceptrons, as a tool to reduce the time required to compute excited state energies. With this approach we predict time-dependent density functional theory (TDDFT) excited state energies of bacteriochlorophylls in the Fenna-Matthews-Olson (FMO) complex. Additionally we compute spectral densities and exciton populations from the predictions. Different methods to determine multi-layer perc"},"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":"1511.07883","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2015-11-24T21:01:01Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"0da53d60058ad4f8cef0c01101ab3e0d2b1d8afee8e6247840207fa499f0daa3","abstract_canon_sha256":"ec29a33d2d249ed47a20126d3373afeddd0d10dd2d63beec3c57fb02d0b7b7d8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:13:16.194323Z","signature_b64":"hmdMygO0CWSbSdHgWN66HxYE3dVnPPcrXwmYUDClIU/C+i5Oq0DsiN2qK+kTc/kmbcPH1Aaazt+cdQtEIFr2Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d284945aa6caa06cfb9d25754b309438cba80b044cf1073149460a23c79d914d","last_reissued_at":"2026-05-18T01:13:16.193795Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:13:16.193795Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine Learning Exciton Dynamics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"Al\\'an Aspuru-Guzik, Edward Pyzer-Knapp, Florian H\\\"ase, St\\'ephanie Valleau","submitted_at":"2015-11-24T21:01:01Z","abstract_excerpt":"Obtaining the exciton dynamics of large photosynthetic complexes by using mixed quantum mechanics/molecular mechanics (QM/MM) is computationally demanding. We propose a machine learning technique, multi-layer perceptrons, as a tool to reduce the time required to compute excited state energies. With this approach we predict time-dependent density functional theory (TDDFT) excited state energies of bacteriochlorophylls in the Fenna-Matthews-Olson (FMO) complex. Additionally we compute spectral densities and exciton populations from the predictions. Different methods to determine multi-layer perc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.07883","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":"1511.07883","created_at":"2026-05-18T01:13:16.193877+00:00"},{"alias_kind":"arxiv_version","alias_value":"1511.07883v1","created_at":"2026-05-18T01:13:16.193877+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.07883","created_at":"2026-05-18T01:13:16.193877+00:00"},{"alias_kind":"pith_short_12","alias_value":"2KCJIWVGZKQG","created_at":"2026-05-18T12:28:59.999130+00:00"},{"alias_kind":"pith_short_16","alias_value":"2KCJIWVGZKQGZ645","created_at":"2026-05-18T12:28:59.999130+00:00"},{"alias_kind":"pith_short_8","alias_value":"2KCJIWVG","created_at":"2026-05-18T12:28:59.999130+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/2KCJIWVGZKQGZ645EV2UWMEUHD","json":"https://pith.science/pith/2KCJIWVGZKQGZ645EV2UWMEUHD.json","graph_json":"https://pith.science/api/pith-number/2KCJIWVGZKQGZ645EV2UWMEUHD/graph.json","events_json":"https://pith.science/api/pith-number/2KCJIWVGZKQGZ645EV2UWMEUHD/events.json","paper":"https://pith.science/paper/2KCJIWVG"},"agent_actions":{"view_html":"https://pith.science/pith/2KCJIWVGZKQGZ645EV2UWMEUHD","download_json":"https://pith.science/pith/2KCJIWVGZKQGZ645EV2UWMEUHD.json","view_paper":"https://pith.science/paper/2KCJIWVG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1511.07883&json=true","fetch_graph":"https://pith.science/api/pith-number/2KCJIWVGZKQGZ645EV2UWMEUHD/graph.json","fetch_events":"https://pith.science/api/pith-number/2KCJIWVGZKQGZ645EV2UWMEUHD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2KCJIWVGZKQGZ645EV2UWMEUHD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2KCJIWVGZKQGZ645EV2UWMEUHD/action/storage_attestation","attest_author":"https://pith.science/pith/2KCJIWVGZKQGZ645EV2UWMEUHD/action/author_attestation","sign_citation":"https://pith.science/pith/2KCJIWVGZKQGZ645EV2UWMEUHD/action/citation_signature","submit_replication":"https://pith.science/pith/2KCJIWVGZKQGZ645EV2UWMEUHD/action/replication_record"}},"created_at":"2026-05-18T01:13:16.193877+00:00","updated_at":"2026-05-18T01:13:16.193877+00:00"}