{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:775JVY3FIK7EQF2NXTMUCSN6PX","short_pith_number":"pith:775JVY3F","schema_version":"1.0","canonical_sha256":"fffa9ae36542be48174dbcd94149be7dfa24aca629d230ed6bfb8ca0d2a71cfa","source":{"kind":"arxiv","id":"2503.02209","version":1},"attestation_state":"computed","paper":{"title":"CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","physics.comp-ph"],"primary_cat":"cs.LG","authors_text":"Kanta Ono, Ryo Igarashi, Tatsunori Taniai, Yoshitaka Ushiku, Yusei Ito","submitted_at":"2025-03-04T02:40:49Z","abstract_excerpt":"Crystal structure modeling with graph neural networks is essential for various applications in materials informatics, and capturing SE(3)-invariant geometric features is a fundamental requirement for these networks. A straightforward approach is to model with orientation-standardized structures through structure-aligned coordinate systems, or\"frames.\" However, unlike molecules, determining frames for crystal structures is challenging due to their infinite and highly symmetric nature. In particular, existing methods rely on a statically fixed frame for each structure, determined solely by its s"},"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":"2503.02209","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-04T02:40:49Z","cross_cats_sorted":["cond-mat.mtrl-sci","physics.comp-ph"],"title_canon_sha256":"bbef35ae5f549eeb43755b33f0c4879066bb902818a41ce6772ea40caee74b59","abstract_canon_sha256":"058846961354d9001e35e28fbf44f68ebafbc47d49edfd38fea45dcf1fbcfff6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:23:39.366594Z","signature_b64":"nBJc8iHvK9W9KrioQUIUsZVfLprGCQvqxnjn6gxC/lRE/1ZU6uaiBEYiObwUYmsZX3/U8tPYISlUgIbZ/n7mCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fffa9ae36542be48174dbcd94149be7dfa24aca629d230ed6bfb8ca0d2a71cfa","last_reissued_at":"2026-07-05T10:23:39.365763Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:23:39.365763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","physics.comp-ph"],"primary_cat":"cs.LG","authors_text":"Kanta Ono, Ryo Igarashi, Tatsunori Taniai, Yoshitaka Ushiku, Yusei Ito","submitted_at":"2025-03-04T02:40:49Z","abstract_excerpt":"Crystal structure modeling with graph neural networks is essential for various applications in materials informatics, and capturing SE(3)-invariant geometric features is a fundamental requirement for these networks. A straightforward approach is to model with orientation-standardized structures through structure-aligned coordinate systems, or\"frames.\" However, unlike molecules, determining frames for crystal structures is challenging due to their infinite and highly symmetric nature. In particular, existing methods rely on a statically fixed frame for each structure, determined solely by its s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.02209","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/2503.02209/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":"2503.02209","created_at":"2026-07-05T10:23:39.365852+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.02209v1","created_at":"2026-07-05T10:23:39.365852+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.02209","created_at":"2026-07-05T10:23:39.365852+00:00"},{"alias_kind":"pith_short_12","alias_value":"775JVY3FIK7E","created_at":"2026-07-05T10:23:39.365852+00:00"},{"alias_kind":"pith_short_16","alias_value":"775JVY3FIK7EQF2N","created_at":"2026-07-05T10:23:39.365852+00:00"},{"alias_kind":"pith_short_8","alias_value":"775JVY3F","created_at":"2026-07-05T10:23:39.365852+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.02748","citing_title":"ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction","ref_index":2014,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/775JVY3FIK7EQF2NXTMUCSN6PX","json":"https://pith.science/pith/775JVY3FIK7EQF2NXTMUCSN6PX.json","graph_json":"https://pith.science/api/pith-number/775JVY3FIK7EQF2NXTMUCSN6PX/graph.json","events_json":"https://pith.science/api/pith-number/775JVY3FIK7EQF2NXTMUCSN6PX/events.json","paper":"https://pith.science/paper/775JVY3F"},"agent_actions":{"view_html":"https://pith.science/pith/775JVY3FIK7EQF2NXTMUCSN6PX","download_json":"https://pith.science/pith/775JVY3FIK7EQF2NXTMUCSN6PX.json","view_paper":"https://pith.science/paper/775JVY3F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.02209&json=true","fetch_graph":"https://pith.science/api/pith-number/775JVY3FIK7EQF2NXTMUCSN6PX/graph.json","fetch_events":"https://pith.science/api/pith-number/775JVY3FIK7EQF2NXTMUCSN6PX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/775JVY3FIK7EQF2NXTMUCSN6PX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/775JVY3FIK7EQF2NXTMUCSN6PX/action/storage_attestation","attest_author":"https://pith.science/pith/775JVY3FIK7EQF2NXTMUCSN6PX/action/author_attestation","sign_citation":"https://pith.science/pith/775JVY3FIK7EQF2NXTMUCSN6PX/action/citation_signature","submit_replication":"https://pith.science/pith/775JVY3FIK7EQF2NXTMUCSN6PX/action/replication_record"}},"created_at":"2026-07-05T10:23:39.365852+00:00","updated_at":"2026-07-05T10:23:39.365852+00:00"}