{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2ZPS5TOVDCN3LQOGPKKX5USUSQ","short_pith_number":"pith:2ZPS5TOV","schema_version":"1.0","canonical_sha256":"d65f2ecdd5189bb5c1c67a957ed25494387ec13377009f80deea46174c08cab4","source":{"kind":"arxiv","id":"2505.20299","version":1},"attestation_state":"computed","paper":{"title":"MetamatBench: Integrating Heterogeneous Data, Computational Tools, and Visual Interface for Metamaterial Discovery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"physics.optics","authors_text":"Dawei Zhou, Haohui Wang, Jianpeng Chen, Jingru Gan, Jingyuan Qi, Junkai Zhang, Lifu Huang, Ling Li, Muhao Chen, Tingwei Chen, Wangzhi Zhan, Wei Wang, Zian Jia","submitted_at":"2025-05-08T19:23:59Z","abstract_excerpt":"Metamaterials, engineered materials with architected structures across multiple length scales, offer unprecedented and tunable mechanical properties that surpass those of conventional materials. However, leveraging advanced machine learning (ML) for metamaterial discovery is hindered by three fundamental challenges: (C1) Data Heterogeneity Challenge arises from heterogeneous data sources, heterogeneous composition scales, and heterogeneous structure categories; (C2) Model Complexity Challenge stems from the intricate geometric constraints of ML models, which complicate their adaptation to meta"},"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":"2505.20299","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.optics","submitted_at":"2025-05-08T19:23:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5a2891cb3441788b05bf2af5adcd47d06d41f6dee2e2f7139e3da2fe590b0af0","abstract_canon_sha256":"5be364b3f815d983f0a826dcf9a9dc0757b6bd1e31531b20f1a681bbdc14523e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:10.885851Z","signature_b64":"au5QxCMR7cHepmsrqNT6vaB92xXEFgCPXY06C2/Wh1hNWTveGH0pQwE1g+ZiYPw18OW3/b4YT3wa/bh7A9BKAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d65f2ecdd5189bb5c1c67a957ed25494387ec13377009f80deea46174c08cab4","last_reissued_at":"2026-07-05T11:10:10.885322Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:10.885322Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MetamatBench: Integrating Heterogeneous Data, Computational Tools, and Visual Interface for Metamaterial Discovery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"physics.optics","authors_text":"Dawei Zhou, Haohui Wang, Jianpeng Chen, Jingru Gan, Jingyuan Qi, Junkai Zhang, Lifu Huang, Ling Li, Muhao Chen, Tingwei Chen, Wangzhi Zhan, Wei Wang, Zian Jia","submitted_at":"2025-05-08T19:23:59Z","abstract_excerpt":"Metamaterials, engineered materials with architected structures across multiple length scales, offer unprecedented and tunable mechanical properties that surpass those of conventional materials. However, leveraging advanced machine learning (ML) for metamaterial discovery is hindered by three fundamental challenges: (C1) Data Heterogeneity Challenge arises from heterogeneous data sources, heterogeneous composition scales, and heterogeneous structure categories; (C2) Model Complexity Challenge stems from the intricate geometric constraints of ML models, which complicate their adaptation to meta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.20299","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/2505.20299/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":"2505.20299","created_at":"2026-07-05T11:10:10.885401+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.20299v1","created_at":"2026-07-05T11:10:10.885401+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.20299","created_at":"2026-07-05T11:10:10.885401+00:00"},{"alias_kind":"pith_short_12","alias_value":"2ZPS5TOVDCN3","created_at":"2026-07-05T11:10:10.885401+00:00"},{"alias_kind":"pith_short_16","alias_value":"2ZPS5TOVDCN3LQOG","created_at":"2026-07-05T11:10:10.885401+00:00"},{"alias_kind":"pith_short_8","alias_value":"2ZPS5TOV","created_at":"2026-07-05T11:10:10.885401+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.15722","citing_title":"UniMate: A Unified Model for Mechanical Metamaterial Generation, Property Prediction, and Condition Confirmation","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2ZPS5TOVDCN3LQOGPKKX5USUSQ","json":"https://pith.science/pith/2ZPS5TOVDCN3LQOGPKKX5USUSQ.json","graph_json":"https://pith.science/api/pith-number/2ZPS5TOVDCN3LQOGPKKX5USUSQ/graph.json","events_json":"https://pith.science/api/pith-number/2ZPS5TOVDCN3LQOGPKKX5USUSQ/events.json","paper":"https://pith.science/paper/2ZPS5TOV"},"agent_actions":{"view_html":"https://pith.science/pith/2ZPS5TOVDCN3LQOGPKKX5USUSQ","download_json":"https://pith.science/pith/2ZPS5TOVDCN3LQOGPKKX5USUSQ.json","view_paper":"https://pith.science/paper/2ZPS5TOV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.20299&json=true","fetch_graph":"https://pith.science/api/pith-number/2ZPS5TOVDCN3LQOGPKKX5USUSQ/graph.json","fetch_events":"https://pith.science/api/pith-number/2ZPS5TOVDCN3LQOGPKKX5USUSQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2ZPS5TOVDCN3LQOGPKKX5USUSQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2ZPS5TOVDCN3LQOGPKKX5USUSQ/action/storage_attestation","attest_author":"https://pith.science/pith/2ZPS5TOVDCN3LQOGPKKX5USUSQ/action/author_attestation","sign_citation":"https://pith.science/pith/2ZPS5TOVDCN3LQOGPKKX5USUSQ/action/citation_signature","submit_replication":"https://pith.science/pith/2ZPS5TOVDCN3LQOGPKKX5USUSQ/action/replication_record"}},"created_at":"2026-07-05T11:10:10.885401+00:00","updated_at":"2026-07-05T11:10:10.885401+00:00"}