{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Q62G5HSZI5ASEGCNER4CPE3UUK","short_pith_number":"pith:Q62G5HSZ","schema_version":"1.0","canonical_sha256":"87b46e9e59474122184d2478279374a2bf4545e1ee94260c76f377be0f0e69aa","source":{"kind":"arxiv","id":"2412.04670","version":1},"attestation_state":"computed","paper":{"title":"Lattice Lingo: Effect of Textual Detail on Multimodal Learning for Property Prediction of Crystals","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"ChangYoung Park, Jaewan Lee, Mrigi Munjal, Sehui Han","submitted_at":"2024-12-05T23:43:51Z","abstract_excerpt":"Most prediction models for crystal properties employ a unimodal perspective, with graph-based representations, overlooking important non-local information that affects crystal properties. Some recent studies explore the impact of integrating graph and textual information on crystal property predictions to provide the model with this \"missing\" information by concatenation of embeddings. However, such studies do not evaluate which type of textual information is actually beneficial. We concatenate graph representations with text representations derived from textual descriptions with varying level"},"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":"2412.04670","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-12-05T23:43:51Z","cross_cats_sorted":[],"title_canon_sha256":"68e7d0e733a7f8bc7366183792c9abd56ebe467ba6ae1839d255f9efd96b9603","abstract_canon_sha256":"299f9edc67e4b4c2bc40697556a922861b9f5669e36c50fd32a07eb6ffdf1a5a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:45:21.728466Z","signature_b64":"tS768PqX4cE1XgvIAJVjJe9p257cOSBQOIw1Vti9Nuthiu0D52OYk55gZtCtXSSeCIzHU3kykmIe4EPp1pOfBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"87b46e9e59474122184d2478279374a2bf4545e1ee94260c76f377be0f0e69aa","last_reissued_at":"2026-07-05T09:45:21.727968Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:45:21.727968Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Lattice Lingo: Effect of Textual Detail on Multimodal Learning for Property Prediction of Crystals","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"ChangYoung Park, Jaewan Lee, Mrigi Munjal, Sehui Han","submitted_at":"2024-12-05T23:43:51Z","abstract_excerpt":"Most prediction models for crystal properties employ a unimodal perspective, with graph-based representations, overlooking important non-local information that affects crystal properties. Some recent studies explore the impact of integrating graph and textual information on crystal property predictions to provide the model with this \"missing\" information by concatenation of embeddings. However, such studies do not evaluate which type of textual information is actually beneficial. We concatenate graph representations with text representations derived from textual descriptions with varying level"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04670","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/2412.04670/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":"2412.04670","created_at":"2026-07-05T09:45:21.728033+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.04670v1","created_at":"2026-07-05T09:45:21.728033+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04670","created_at":"2026-07-05T09:45:21.728033+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q62G5HSZI5AS","created_at":"2026-07-05T09:45:21.728033+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q62G5HSZI5ASEGCN","created_at":"2026-07-05T09:45:21.728033+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q62G5HSZ","created_at":"2026-07-05T09:45:21.728033+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.06836","citing_title":"CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q62G5HSZI5ASEGCNER4CPE3UUK","json":"https://pith.science/pith/Q62G5HSZI5ASEGCNER4CPE3UUK.json","graph_json":"https://pith.science/api/pith-number/Q62G5HSZI5ASEGCNER4CPE3UUK/graph.json","events_json":"https://pith.science/api/pith-number/Q62G5HSZI5ASEGCNER4CPE3UUK/events.json","paper":"https://pith.science/paper/Q62G5HSZ"},"agent_actions":{"view_html":"https://pith.science/pith/Q62G5HSZI5ASEGCNER4CPE3UUK","download_json":"https://pith.science/pith/Q62G5HSZI5ASEGCNER4CPE3UUK.json","view_paper":"https://pith.science/paper/Q62G5HSZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.04670&json=true","fetch_graph":"https://pith.science/api/pith-number/Q62G5HSZI5ASEGCNER4CPE3UUK/graph.json","fetch_events":"https://pith.science/api/pith-number/Q62G5HSZI5ASEGCNER4CPE3UUK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q62G5HSZI5ASEGCNER4CPE3UUK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q62G5HSZI5ASEGCNER4CPE3UUK/action/storage_attestation","attest_author":"https://pith.science/pith/Q62G5HSZI5ASEGCNER4CPE3UUK/action/author_attestation","sign_citation":"https://pith.science/pith/Q62G5HSZI5ASEGCNER4CPE3UUK/action/citation_signature","submit_replication":"https://pith.science/pith/Q62G5HSZI5ASEGCNER4CPE3UUK/action/replication_record"}},"created_at":"2026-07-05T09:45:21.728033+00:00","updated_at":"2026-07-05T09:45:21.728033+00:00"}