{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LUJG4OJNKN2OGLDE2NFPABIG7C","short_pith_number":"pith:LUJG4OJN","schema_version":"1.0","canonical_sha256":"5d126e392d5374e32c64d34af00506f885e4d3c95a612fab2f8e927debce8881","source":{"kind":"arxiv","id":"2407.04557","version":1},"attestation_state":"computed","paper":{"title":"Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Abhijatmedhi Chotrattanapituk, Bowen Han, Mingda Li, Mouyang Cheng, Nguyen Tuan Hung, Robert J. Cava, Ryotaro Okabe, Tommi S. Jaakkola, Weiwei Xie, Xiang Fu, Yao Wang, Yongqiang Cheng","submitted_at":"2024-07-05T14:42:54Z","abstract_excerpt":"Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searching for new quantum materials. Recent advancements in machine-learning-based generative models, particularly diffusion models, show great promise for generating new, stable materials. However, integrating geometric patterns into materials generation remains a challenge. Here, we introduce Structural Constraint Integration in the GENerative model (SCIGEN). Our approach can modify any trained generative diffusion mode"},"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":"2407.04557","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-07-05T14:42:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"73b0666fa027a077517397f05827cd61e58afe4691ebcbe8b9ce77eb59c304ef","abstract_canon_sha256":"f45c470ef732509e1d9722d1ebab72fa6a8129814493186650a2520c0f4340c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:40:35.457085Z","signature_b64":"Uz9jNKTx9JbIudXsvmKxapZ2C7q5NrQfwDxwayEEVmyobvl9FY3Pfg7btnBM8WOLaus2o3stxvIWkSGf1AToCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d126e392d5374e32c64d34af00506f885e4d3c95a612fab2f8e927debce8881","last_reissued_at":"2026-07-05T08:40:35.456493Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:40:35.456493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Abhijatmedhi Chotrattanapituk, Bowen Han, Mingda Li, Mouyang Cheng, Nguyen Tuan Hung, Robert J. Cava, Ryotaro Okabe, Tommi S. Jaakkola, Weiwei Xie, Xiang Fu, Yao Wang, Yongqiang Cheng","submitted_at":"2024-07-05T14:42:54Z","abstract_excerpt":"Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searching for new quantum materials. Recent advancements in machine-learning-based generative models, particularly diffusion models, show great promise for generating new, stable materials. However, integrating geometric patterns into materials generation remains a challenge. Here, we introduce Structural Constraint Integration in the GENerative model (SCIGEN). Our approach can modify any trained generative diffusion mode"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.04557","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/2407.04557/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":"2407.04557","created_at":"2026-07-05T08:40:35.456566+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.04557v1","created_at":"2026-07-05T08:40:35.456566+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.04557","created_at":"2026-07-05T08:40:35.456566+00:00"},{"alias_kind":"pith_short_12","alias_value":"LUJG4OJNKN2O","created_at":"2026-07-05T08:40:35.456566+00:00"},{"alias_kind":"pith_short_16","alias_value":"LUJG4OJNKN2OGLDE","created_at":"2026-07-05T08:40:35.456566+00:00"},{"alias_kind":"pith_short_8","alias_value":"LUJG4OJN","created_at":"2026-07-05T08:40:35.456566+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.02905","citing_title":"AI-driven materials design: a mini-review","ref_index":60,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LUJG4OJNKN2OGLDE2NFPABIG7C","json":"https://pith.science/pith/LUJG4OJNKN2OGLDE2NFPABIG7C.json","graph_json":"https://pith.science/api/pith-number/LUJG4OJNKN2OGLDE2NFPABIG7C/graph.json","events_json":"https://pith.science/api/pith-number/LUJG4OJNKN2OGLDE2NFPABIG7C/events.json","paper":"https://pith.science/paper/LUJG4OJN"},"agent_actions":{"view_html":"https://pith.science/pith/LUJG4OJNKN2OGLDE2NFPABIG7C","download_json":"https://pith.science/pith/LUJG4OJNKN2OGLDE2NFPABIG7C.json","view_paper":"https://pith.science/paper/LUJG4OJN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.04557&json=true","fetch_graph":"https://pith.science/api/pith-number/LUJG4OJNKN2OGLDE2NFPABIG7C/graph.json","fetch_events":"https://pith.science/api/pith-number/LUJG4OJNKN2OGLDE2NFPABIG7C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LUJG4OJNKN2OGLDE2NFPABIG7C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LUJG4OJNKN2OGLDE2NFPABIG7C/action/storage_attestation","attest_author":"https://pith.science/pith/LUJG4OJNKN2OGLDE2NFPABIG7C/action/author_attestation","sign_citation":"https://pith.science/pith/LUJG4OJNKN2OGLDE2NFPABIG7C/action/citation_signature","submit_replication":"https://pith.science/pith/LUJG4OJNKN2OGLDE2NFPABIG7C/action/replication_record"}},"created_at":"2026-07-05T08:40:35.456566+00:00","updated_at":"2026-07-05T08:40:35.456566+00:00"}