{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NSCBRQTDYXV5MNTEEKSHCWCQTU","short_pith_number":"pith:NSCBRQTD","schema_version":"1.0","canonical_sha256":"6c8418c263c5ebd6366422a47158509d3596403dc2cd6f088eb22b7397559888","source":{"kind":"arxiv","id":"2411.08664","version":1},"attestation_state":"computed","paper":{"title":"UniMat: Unifying Materials Embeddings through Multi-modal Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"cs.LG","authors_text":"Daniel Schweigert, Janghoon Ock, Joseph Montoya, Linda Hung, Santosh K. Suram, Weike Ye","submitted_at":"2024-11-13T14:55:08Z","abstract_excerpt":"Materials science datasets are inherently heterogeneous and are available in different modalities such as characterization spectra, atomic structures, microscopic images, and text-based synthesis conditions. The advancements in multi-modal learning, particularly in vision and language models, have opened new avenues for integrating data in different forms. In this work, we evaluate common techniques in multi-modal learning (alignment and fusion) in unifying some of the most important modalities in materials science: atomic structure, X-ray diffraction patterns (XRD), and composition. We show t"},"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":"2411.08664","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-13T14:55:08Z","cross_cats_sorted":["cond-mat.mtrl-sci"],"title_canon_sha256":"3acfa587f442fd7e1dcb5e625372121f1e41826a995e11657a9584bc5bc04c05","abstract_canon_sha256":"f8efa62f491432e83ec2e5440c7f9d4c9a035947862e8dc8d74eba83808d1e9e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:35:00.524671Z","signature_b64":"pANrw5wKYMZXJy+Fu0kXyOqLHIiu//o51x9qqxZTKbqVPkHROuhaNE/Z/z3uR19mLyBgpUnLONlD1S248fUiCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c8418c263c5ebd6366422a47158509d3596403dc2cd6f088eb22b7397559888","last_reissued_at":"2026-07-05T09:35:00.524190Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:35:00.524190Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"UniMat: Unifying Materials Embeddings through Multi-modal Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"cs.LG","authors_text":"Daniel Schweigert, Janghoon Ock, Joseph Montoya, Linda Hung, Santosh K. Suram, Weike Ye","submitted_at":"2024-11-13T14:55:08Z","abstract_excerpt":"Materials science datasets are inherently heterogeneous and are available in different modalities such as characterization spectra, atomic structures, microscopic images, and text-based synthesis conditions. The advancements in multi-modal learning, particularly in vision and language models, have opened new avenues for integrating data in different forms. In this work, we evaluate common techniques in multi-modal learning (alignment and fusion) in unifying some of the most important modalities in materials science: atomic structure, X-ray diffraction patterns (XRD), and composition. We show t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.08664","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/2411.08664/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":"2411.08664","created_at":"2026-07-05T09:35:00.524250+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.08664v1","created_at":"2026-07-05T09:35:00.524250+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.08664","created_at":"2026-07-05T09:35:00.524250+00:00"},{"alias_kind":"pith_short_12","alias_value":"NSCBRQTDYXV5","created_at":"2026-07-05T09:35:00.524250+00:00"},{"alias_kind":"pith_short_16","alias_value":"NSCBRQTDYXV5MNTE","created_at":"2026-07-05T09:35:00.524250+00:00"},{"alias_kind":"pith_short_8","alias_value":"NSCBRQTD","created_at":"2026-07-05T09:35:00.524250+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.27163","citing_title":"Materials Behavior as Mechanism Ensembles: A Probabilistic Framework for Emergent Behaviors","ref_index":127,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NSCBRQTDYXV5MNTEEKSHCWCQTU","json":"https://pith.science/pith/NSCBRQTDYXV5MNTEEKSHCWCQTU.json","graph_json":"https://pith.science/api/pith-number/NSCBRQTDYXV5MNTEEKSHCWCQTU/graph.json","events_json":"https://pith.science/api/pith-number/NSCBRQTDYXV5MNTEEKSHCWCQTU/events.json","paper":"https://pith.science/paper/NSCBRQTD"},"agent_actions":{"view_html":"https://pith.science/pith/NSCBRQTDYXV5MNTEEKSHCWCQTU","download_json":"https://pith.science/pith/NSCBRQTDYXV5MNTEEKSHCWCQTU.json","view_paper":"https://pith.science/paper/NSCBRQTD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.08664&json=true","fetch_graph":"https://pith.science/api/pith-number/NSCBRQTDYXV5MNTEEKSHCWCQTU/graph.json","fetch_events":"https://pith.science/api/pith-number/NSCBRQTDYXV5MNTEEKSHCWCQTU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NSCBRQTDYXV5MNTEEKSHCWCQTU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NSCBRQTDYXV5MNTEEKSHCWCQTU/action/storage_attestation","attest_author":"https://pith.science/pith/NSCBRQTDYXV5MNTEEKSHCWCQTU/action/author_attestation","sign_citation":"https://pith.science/pith/NSCBRQTDYXV5MNTEEKSHCWCQTU/action/citation_signature","submit_replication":"https://pith.science/pith/NSCBRQTDYXV5MNTEEKSHCWCQTU/action/replication_record"}},"created_at":"2026-07-05T09:35:00.524250+00:00","updated_at":"2026-07-05T09:35:00.524250+00:00"}