{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UNC2LV6YDXVMLPLSYGINO6BC2B","short_pith_number":"pith:UNC2LV6Y","schema_version":"1.0","canonical_sha256":"a345a5d7d81deac5bd72c190d77822d075059612a2d5929a55fb05b6a9397039","source":{"kind":"arxiv","id":"2406.14809","version":1},"attestation_state":"computed","paper":{"title":"Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Brandon K. Phan, Huan Tran, Kuan-Hsuan Shen, Rampi Ramprasad, Rishi Gurnani, Ryan Lively","submitted_at":"2024-06-21T01:18:55Z","abstract_excerpt":"Machine learning (ML) models for predicting gas permeability through polymers have traditionally relied on experimental data. While these models exhibit robustness within familiar chemical domains, reliability wanes when applied to new spaces. To address this challenge, we present a multi-tiered multi-task learning framework empowered with advanced machine-crafted polymer fingerprinting algorithms and data fusion techniques. This framework combines scarce \"high-fidelity\" experimental data with abundant diverse \"low-fidelity\" simulation or synthetic data, resulting in predictive models that dis"},"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":"2406.14809","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-06-21T01:18:55Z","cross_cats_sorted":[],"title_canon_sha256":"20712d2a3a1d387bd1e8a3794cc3f9d7ba554385f743be27e30b24e9104ec49f","abstract_canon_sha256":"c9cb90ccad2bd7ee065686a23641954c0a7fcb83d00513ee58644e79e06354db"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:35:08.732662Z","signature_b64":"OBD5T6UDz/hnZ5n2gQDnZybHuNewpfBzqK5Dqp05ugbkfw6nn4OlspyLaxhbsb7PT4Ptq+aLi072qiZEOd0qCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a345a5d7d81deac5bd72c190d77822d075059612a2d5929a55fb05b6a9397039","last_reissued_at":"2026-07-05T08:35:08.732158Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:35:08.732158Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Brandon K. Phan, Huan Tran, Kuan-Hsuan Shen, Rampi Ramprasad, Rishi Gurnani, Ryan Lively","submitted_at":"2024-06-21T01:18:55Z","abstract_excerpt":"Machine learning (ML) models for predicting gas permeability through polymers have traditionally relied on experimental data. While these models exhibit robustness within familiar chemical domains, reliability wanes when applied to new spaces. To address this challenge, we present a multi-tiered multi-task learning framework empowered with advanced machine-crafted polymer fingerprinting algorithms and data fusion techniques. This framework combines scarce \"high-fidelity\" experimental data with abundant diverse \"low-fidelity\" simulation or synthetic data, resulting in predictive models that dis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.14809","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2406.14809/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2406.14809","created_at":"2026-07-05T08:35:08.732214+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.14809v1","created_at":"2026-07-05T08:35:08.732214+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.14809","created_at":"2026-07-05T08:35:08.732214+00:00"},{"alias_kind":"pith_short_12","alias_value":"UNC2LV6YDXVM","created_at":"2026-07-05T08:35:08.732214+00:00"},{"alias_kind":"pith_short_16","alias_value":"UNC2LV6YDXVMLPLS","created_at":"2026-07-05T08:35:08.732214+00:00"},{"alias_kind":"pith_short_8","alias_value":"UNC2LV6Y","created_at":"2026-07-05T08:35:08.732214+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/UNC2LV6YDXVMLPLSYGINO6BC2B","json":"https://pith.science/pith/UNC2LV6YDXVMLPLSYGINO6BC2B.json","graph_json":"https://pith.science/api/pith-number/UNC2LV6YDXVMLPLSYGINO6BC2B/graph.json","events_json":"https://pith.science/api/pith-number/UNC2LV6YDXVMLPLSYGINO6BC2B/events.json","paper":"https://pith.science/paper/UNC2LV6Y"},"agent_actions":{"view_html":"https://pith.science/pith/UNC2LV6YDXVMLPLSYGINO6BC2B","download_json":"https://pith.science/pith/UNC2LV6YDXVMLPLSYGINO6BC2B.json","view_paper":"https://pith.science/paper/UNC2LV6Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.14809&json=true","fetch_graph":"https://pith.science/api/pith-number/UNC2LV6YDXVMLPLSYGINO6BC2B/graph.json","fetch_events":"https://pith.science/api/pith-number/UNC2LV6YDXVMLPLSYGINO6BC2B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UNC2LV6YDXVMLPLSYGINO6BC2B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UNC2LV6YDXVMLPLSYGINO6BC2B/action/storage_attestation","attest_author":"https://pith.science/pith/UNC2LV6YDXVMLPLSYGINO6BC2B/action/author_attestation","sign_citation":"https://pith.science/pith/UNC2LV6YDXVMLPLSYGINO6BC2B/action/citation_signature","submit_replication":"https://pith.science/pith/UNC2LV6YDXVMLPLSYGINO6BC2B/action/replication_record"}},"created_at":"2026-07-05T08:35:08.732214+00:00","updated_at":"2026-07-05T08:35:08.732214+00:00"}