{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FASGYMZO36TOANEQ5WWYHFDASD","short_pith_number":"pith:FASGYMZO","schema_version":"1.0","canonical_sha256":"28246c332edfa6e03490edad83946090db4d283cced1e8087f353ba4bf4144b1","source":{"kind":"arxiv","id":"2506.00725","version":1},"attestation_state":"computed","paper":{"title":"A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Abhijatmedhi Chotrattanapituk, Bowen Yu, Chu-Liang Fu, Douglas L Abernathy, Eunbi Rha, Mingda Li, Mouyang Cheng, Yongqiang Cheng","submitted_at":"2025-05-31T21:51:51Z","abstract_excerpt":"Defects are ubiquitous in solids and strongly influence materials' mechanical and functional properties. However, non-destructive characterization and quantification of defects, especially when multiple types coexist, remain a long-standing challenge. Here we introduce DefectNet, a foundation machine learning model that predicts the chemical identity and concentration of substitutional point defects with multiple coexisting elements directly from vibrational spectra, specifically phonon density-of-states (PDoS). Trained on over 16,000 simulated spectra from 2,000 semiconductors, DefectNet empl"},"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":"2506.00725","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2025-05-31T21:51:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"908e3263a95327e09b94fe75ed6fa996bd88e2d59f62c3b6e0b08231f5fd2345","abstract_canon_sha256":"4350da6a31e8bc8fe4715ddc71da234597137999af8af1b2404dde8361c7f861"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:39.412309Z","signature_b64":"CBo8igAt54N0ElxdAfBHYE+8inYYWV+Ml+D558aFSwo2roTJcpXbXSXh7+7EEBB8h0lePZTLhDm5+A/eH8guBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"28246c332edfa6e03490edad83946090db4d283cced1e8087f353ba4bf4144b1","last_reissued_at":"2026-07-05T11:13:39.411835Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:39.411835Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Abhijatmedhi Chotrattanapituk, Bowen Yu, Chu-Liang Fu, Douglas L Abernathy, Eunbi Rha, Mingda Li, Mouyang Cheng, Yongqiang Cheng","submitted_at":"2025-05-31T21:51:51Z","abstract_excerpt":"Defects are ubiquitous in solids and strongly influence materials' mechanical and functional properties. However, non-destructive characterization and quantification of defects, especially when multiple types coexist, remain a long-standing challenge. Here we introduce DefectNet, a foundation machine learning model that predicts the chemical identity and concentration of substitutional point defects with multiple coexisting elements directly from vibrational spectra, specifically phonon density-of-states (PDoS). Trained on over 16,000 simulated spectra from 2,000 semiconductors, DefectNet empl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00725","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/2506.00725/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":"2506.00725","created_at":"2026-07-05T11:13:39.411895+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.00725v1","created_at":"2026-07-05T11:13:39.411895+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00725","created_at":"2026-07-05T11:13:39.411895+00:00"},{"alias_kind":"pith_short_12","alias_value":"FASGYMZO36TO","created_at":"2026-07-05T11:13:39.411895+00:00"},{"alias_kind":"pith_short_16","alias_value":"FASGYMZO36TOANEQ","created_at":"2026-07-05T11:13:39.411895+00:00"},{"alias_kind":"pith_short_8","alias_value":"FASGYMZO","created_at":"2026-07-05T11:13:39.411895+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FASGYMZO36TOANEQ5WWYHFDASD","json":"https://pith.science/pith/FASGYMZO36TOANEQ5WWYHFDASD.json","graph_json":"https://pith.science/api/pith-number/FASGYMZO36TOANEQ5WWYHFDASD/graph.json","events_json":"https://pith.science/api/pith-number/FASGYMZO36TOANEQ5WWYHFDASD/events.json","paper":"https://pith.science/paper/FASGYMZO"},"agent_actions":{"view_html":"https://pith.science/pith/FASGYMZO36TOANEQ5WWYHFDASD","download_json":"https://pith.science/pith/FASGYMZO36TOANEQ5WWYHFDASD.json","view_paper":"https://pith.science/paper/FASGYMZO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.00725&json=true","fetch_graph":"https://pith.science/api/pith-number/FASGYMZO36TOANEQ5WWYHFDASD/graph.json","fetch_events":"https://pith.science/api/pith-number/FASGYMZO36TOANEQ5WWYHFDASD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FASGYMZO36TOANEQ5WWYHFDASD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FASGYMZO36TOANEQ5WWYHFDASD/action/storage_attestation","attest_author":"https://pith.science/pith/FASGYMZO36TOANEQ5WWYHFDASD/action/author_attestation","sign_citation":"https://pith.science/pith/FASGYMZO36TOANEQ5WWYHFDASD/action/citation_signature","submit_replication":"https://pith.science/pith/FASGYMZO36TOANEQ5WWYHFDASD/action/replication_record"}},"created_at":"2026-07-05T11:13:39.411895+00:00","updated_at":"2026-07-05T11:13:39.411895+00:00"}