{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:C7HHQK4A5DPMD2EKS3MNFBYFEG","short_pith_number":"pith:C7HHQK4A","schema_version":"1.0","canonical_sha256":"17ce782b80e8dec1e88a96d8d2870521a35cdcafe5e25cb6397f8fbbdc76c257","source":{"kind":"arxiv","id":"1905.06945","version":1},"attestation_state":"computed","paper":{"title":"Uncertainty quantification of molecular property prediction using Bayesian neural network models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"physics.chem-ph","authors_text":"Seongok Ryu, Woo Youn Kim, Yongchan Kwon","submitted_at":"2018-11-19T02:22:56Z","abstract_excerpt":"In chemistry, deep neural network models have been increasingly utilized in a variety of applications such as molecular property predictions, novel molecule designs, and planning chemical reactions. Despite the rapid increase in the use of state-of-the-art models and algorithms, deep neural network models often produce poor predictions in real applications because model performance is highly dependent on the quality of training data. In the field of molecular analysis, data are mostly obtained from either complicated chemical experiments or approximate mathematical equations, and then quality "},"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":"1905.06945","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2018-11-19T02:22:56Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"5bf51e981462ab2fc916dcc777e95752297b5760f2e006dc5a1f81ba7db9e0c0","abstract_canon_sha256":"bda7e84807ac3a7730e1b459085486ad2cef8292fb85c94c6345c9c6d4c12019"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:58.450866Z","signature_b64":"GhTy6PwKN8pW859uKxMsmbZ+77JnM9rWrDRO6FzJdjkeAekiHwqRgswker87UvHbDNipgtoVOYG/oAwAO0AmDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17ce782b80e8dec1e88a96d8d2870521a35cdcafe5e25cb6397f8fbbdc76c257","last_reissued_at":"2026-05-17T23:45:58.450039Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:58.450039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Uncertainty quantification of molecular property prediction using Bayesian neural network models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"physics.chem-ph","authors_text":"Seongok Ryu, Woo Youn Kim, Yongchan Kwon","submitted_at":"2018-11-19T02:22:56Z","abstract_excerpt":"In chemistry, deep neural network models have been increasingly utilized in a variety of applications such as molecular property predictions, novel molecule designs, and planning chemical reactions. Despite the rapid increase in the use of state-of-the-art models and algorithms, deep neural network models often produce poor predictions in real applications because model performance is highly dependent on the quality of training data. In the field of molecular analysis, data are mostly obtained from either complicated chemical experiments or approximate mathematical equations, and then quality "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.06945","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":"1905.06945","created_at":"2026-05-17T23:45:58.450104+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.06945v1","created_at":"2026-05-17T23:45:58.450104+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.06945","created_at":"2026-05-17T23:45:58.450104+00:00"},{"alias_kind":"pith_short_12","alias_value":"C7HHQK4A5DPM","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"C7HHQK4A5DPMD2EK","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"C7HHQK4A","created_at":"2026-05-18T12:32:16.446611+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/C7HHQK4A5DPMD2EKS3MNFBYFEG","json":"https://pith.science/pith/C7HHQK4A5DPMD2EKS3MNFBYFEG.json","graph_json":"https://pith.science/api/pith-number/C7HHQK4A5DPMD2EKS3MNFBYFEG/graph.json","events_json":"https://pith.science/api/pith-number/C7HHQK4A5DPMD2EKS3MNFBYFEG/events.json","paper":"https://pith.science/paper/C7HHQK4A"},"agent_actions":{"view_html":"https://pith.science/pith/C7HHQK4A5DPMD2EKS3MNFBYFEG","download_json":"https://pith.science/pith/C7HHQK4A5DPMD2EKS3MNFBYFEG.json","view_paper":"https://pith.science/paper/C7HHQK4A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.06945&json=true","fetch_graph":"https://pith.science/api/pith-number/C7HHQK4A5DPMD2EKS3MNFBYFEG/graph.json","fetch_events":"https://pith.science/api/pith-number/C7HHQK4A5DPMD2EKS3MNFBYFEG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C7HHQK4A5DPMD2EKS3MNFBYFEG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C7HHQK4A5DPMD2EKS3MNFBYFEG/action/storage_attestation","attest_author":"https://pith.science/pith/C7HHQK4A5DPMD2EKS3MNFBYFEG/action/author_attestation","sign_citation":"https://pith.science/pith/C7HHQK4A5DPMD2EKS3MNFBYFEG/action/citation_signature","submit_replication":"https://pith.science/pith/C7HHQK4A5DPMD2EKS3MNFBYFEG/action/replication_record"}},"created_at":"2026-05-17T23:45:58.450104+00:00","updated_at":"2026-05-17T23:45:58.450104+00:00"}