{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:FEAC2ZIOOPGFO5EP6IULZMDLHI","short_pith_number":"pith:FEAC2ZIO","schema_version":"1.0","canonical_sha256":"29002d650e73cc57748ff228bcb06b3a32580c8447867b22aadf380ccebc991f","source":{"kind":"arxiv","id":"1905.03928","version":1},"attestation_state":"computed","paper":{"title":"A Deep Learning Model for Atomic Structures Prediction Using X-ray Absorption Spectroscopic Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"physics.comp-ph","authors_text":"Liang Li, Maria K. Y. Chan, Mindren Lu","submitted_at":"2019-05-10T04:08:40Z","abstract_excerpt":"A deep neural network (DNN) model consisting of two hidden layers was proposed for predicting the immediate environments of specific atoms based on X-ray absorption near-edge spectra (XANES). The output layer of the DNN can be adjusted to form a classifier or regressor, to predict the local and overall coordination environments, respectively. Using Li3FeO3.5 as a model system, it was demonstrated that the prediction accuracy of the DNN classifier is higher than 98%, and the predictions of the DNN regressor also showed notable agreement with the ground truth. Therefore, despite its simplicity, "},"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":"1905.03928","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2019-05-10T04:08:40Z","cross_cats_sorted":["cond-mat.mtrl-sci"],"title_canon_sha256":"d61d12a38b3bfc4195342273f7b5446c6d6648a5d9e15648b4ff9e14b40c4e61","abstract_canon_sha256":"5239d0832eb1459d1af6d4e556e13c7c27f78c5d825f488959ae69c6fc4b435e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:35.163292Z","signature_b64":"z4ZBUXrzzZav2SjVspQ98r2xnrfN24HTsql77mz4pnKi1MI8uqdkGnNVi0gbx7CtOQSHbFrKyEkq8esajoA8BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"29002d650e73cc57748ff228bcb06b3a32580c8447867b22aadf380ccebc991f","last_reissued_at":"2026-05-17T23:46:35.162706Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:35.162706Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Deep Learning Model for Atomic Structures Prediction Using X-ray Absorption Spectroscopic Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"physics.comp-ph","authors_text":"Liang Li, Maria K. Y. Chan, Mindren Lu","submitted_at":"2019-05-10T04:08:40Z","abstract_excerpt":"A deep neural network (DNN) model consisting of two hidden layers was proposed for predicting the immediate environments of specific atoms based on X-ray absorption near-edge spectra (XANES). The output layer of the DNN can be adjusted to form a classifier or regressor, to predict the local and overall coordination environments, respectively. Using Li3FeO3.5 as a model system, it was demonstrated that the prediction accuracy of the DNN classifier is higher than 98%, and the predictions of the DNN regressor also showed notable agreement with the ground truth. Therefore, despite its simplicity, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.03928","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":"1905.03928","created_at":"2026-05-17T23:46:35.162789+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.03928v1","created_at":"2026-05-17T23:46:35.162789+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.03928","created_at":"2026-05-17T23:46:35.162789+00:00"},{"alias_kind":"pith_short_12","alias_value":"FEAC2ZIOOPGF","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"FEAC2ZIOOPGFO5EP","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"FEAC2ZIO","created_at":"2026-05-18T12:33:15.570797+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/FEAC2ZIOOPGFO5EP6IULZMDLHI","json":"https://pith.science/pith/FEAC2ZIOOPGFO5EP6IULZMDLHI.json","graph_json":"https://pith.science/api/pith-number/FEAC2ZIOOPGFO5EP6IULZMDLHI/graph.json","events_json":"https://pith.science/api/pith-number/FEAC2ZIOOPGFO5EP6IULZMDLHI/events.json","paper":"https://pith.science/paper/FEAC2ZIO"},"agent_actions":{"view_html":"https://pith.science/pith/FEAC2ZIOOPGFO5EP6IULZMDLHI","download_json":"https://pith.science/pith/FEAC2ZIOOPGFO5EP6IULZMDLHI.json","view_paper":"https://pith.science/paper/FEAC2ZIO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.03928&json=true","fetch_graph":"https://pith.science/api/pith-number/FEAC2ZIOOPGFO5EP6IULZMDLHI/graph.json","fetch_events":"https://pith.science/api/pith-number/FEAC2ZIOOPGFO5EP6IULZMDLHI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FEAC2ZIOOPGFO5EP6IULZMDLHI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FEAC2ZIOOPGFO5EP6IULZMDLHI/action/storage_attestation","attest_author":"https://pith.science/pith/FEAC2ZIOOPGFO5EP6IULZMDLHI/action/author_attestation","sign_citation":"https://pith.science/pith/FEAC2ZIOOPGFO5EP6IULZMDLHI/action/citation_signature","submit_replication":"https://pith.science/pith/FEAC2ZIOOPGFO5EP6IULZMDLHI/action/replication_record"}},"created_at":"2026-05-17T23:46:35.162789+00:00","updated_at":"2026-05-17T23:46:35.162789+00:00"}