{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:AVFE7WREDKRQMXZXEWOK2V466M","short_pith_number":"pith:AVFE7WRE","schema_version":"1.0","canonical_sha256":"054a4fda241aa3065f37259cad579ef3393c6d3d47a6b9bc55f0eab666f341c2","source":{"kind":"arxiv","id":"2312.16473","version":1},"attestation_state":"computed","paper":{"title":"MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"cs.LG","authors_text":"Hengrui Zhang, James M. Rondinelli, Jie Chen, Wei Chen","submitted_at":"2023-12-27T08:46:14Z","abstract_excerpt":"Recent advances in machine learning (ML) have expedited materials discovery and design. One significant challenge faced in ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here we present MolSets, a specialize"},"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":"2312.16473","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-27T08:46:14Z","cross_cats_sorted":["cond-mat.mtrl-sci"],"title_canon_sha256":"6f5bb6cd8656dfe1bd715b9e56dc5865abc8b518bbdb1a21f49f6a46342ddcd1","abstract_canon_sha256":"e97fd36a70bc229b832b576c19ae61ea443f45e0a9a04e24ecbb0079a6770606"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:30:49.529126Z","signature_b64":"mLeEIPLBE+6e2NtEarwWqMaUp/lPS+7ywGmt8jLdAf30JEBs8d7/NKKXh/9C249wDE2uDwqeWf4b5/hZFoieDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"054a4fda241aa3065f37259cad579ef3393c6d3d47a6b9bc55f0eab666f341c2","last_reissued_at":"2026-07-05T08:30:49.528637Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:30:49.528637Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"cs.LG","authors_text":"Hengrui Zhang, James M. Rondinelli, Jie Chen, Wei Chen","submitted_at":"2023-12-27T08:46:14Z","abstract_excerpt":"Recent advances in machine learning (ML) have expedited materials discovery and design. One significant challenge faced in ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here we present MolSets, a specialize"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.16473","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/2312.16473/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":"2312.16473","created_at":"2026-07-05T08:30:49.528709+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.16473v1","created_at":"2026-07-05T08:30:49.528709+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.16473","created_at":"2026-07-05T08:30:49.528709+00:00"},{"alias_kind":"pith_short_12","alias_value":"AVFE7WREDKRQ","created_at":"2026-07-05T08:30:49.528709+00:00"},{"alias_kind":"pith_short_16","alias_value":"AVFE7WREDKRQMXZX","created_at":"2026-07-05T08:30:49.528709+00:00"},{"alias_kind":"pith_short_8","alias_value":"AVFE7WRE","created_at":"2026-07-05T08:30:49.528709+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/AVFE7WREDKRQMXZXEWOK2V466M","json":"https://pith.science/pith/AVFE7WREDKRQMXZXEWOK2V466M.json","graph_json":"https://pith.science/api/pith-number/AVFE7WREDKRQMXZXEWOK2V466M/graph.json","events_json":"https://pith.science/api/pith-number/AVFE7WREDKRQMXZXEWOK2V466M/events.json","paper":"https://pith.science/paper/AVFE7WRE"},"agent_actions":{"view_html":"https://pith.science/pith/AVFE7WREDKRQMXZXEWOK2V466M","download_json":"https://pith.science/pith/AVFE7WREDKRQMXZXEWOK2V466M.json","view_paper":"https://pith.science/paper/AVFE7WRE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.16473&json=true","fetch_graph":"https://pith.science/api/pith-number/AVFE7WREDKRQMXZXEWOK2V466M/graph.json","fetch_events":"https://pith.science/api/pith-number/AVFE7WREDKRQMXZXEWOK2V466M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AVFE7WREDKRQMXZXEWOK2V466M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AVFE7WREDKRQMXZXEWOK2V466M/action/storage_attestation","attest_author":"https://pith.science/pith/AVFE7WREDKRQMXZXEWOK2V466M/action/author_attestation","sign_citation":"https://pith.science/pith/AVFE7WREDKRQMXZXEWOK2V466M/action/citation_signature","submit_replication":"https://pith.science/pith/AVFE7WREDKRQMXZXEWOK2V466M/action/replication_record"}},"created_at":"2026-07-05T08:30:49.528709+00:00","updated_at":"2026-07-05T08:30:49.528709+00:00"}