{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:R7325HTJIDNZBPPJ5BR3CSQFLO","short_pith_number":"pith:R7325HTJ","schema_version":"1.0","canonical_sha256":"8ff7ae9e6940db90bde9e863b14a055ba620cab5da438ab6aef0cd1e2ae948a8","source":{"kind":"arxiv","id":"2410.19980","version":1},"attestation_state":"computed","paper":{"title":"Molecular Fingerprints of Ice Surfaces in Sum Frequency Generation Spectra: a First Principles Machine Learning Study","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.soft"],"primary_cat":"physics.chem-ph","authors_text":"Davide Donadio, John T. Fourkas, Marcos F. Calegari Andrade, Margaret L. Berrens, Tuan Anh Pham","submitted_at":"2024-10-25T21:40:46Z","abstract_excerpt":"Understanding the molecular-level structure and dynamics of ice surfaces is crucial for deciphering several chemical, physical, and atmospheric processes. Vibrational sum-frequency generation (SFG) spectroscopy is the most prominent tool for probing the molecular-level structure of the air--ice interface as it is a surface-specific technique, but the molecular interpretation of SFG spectra is challenging. This study utilizes a machine-learning potential, along with dipole and polarizability models trained on ab initio data, to calculate the SFG spectrum of the air--ice interface. At temperatur"},"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":"2410.19980","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2024-10-25T21:40:46Z","cross_cats_sorted":["cond-mat.soft"],"title_canon_sha256":"9d5f61dbe8b34fe56ace9ab1fc08824144dd13c1e9cb67e34e19f267c469236a","abstract_canon_sha256":"f946d3029a0147e6134eec33bb3a42c896fd1a30c43e855e734f19806fbde743"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:26:36.723797Z","signature_b64":"nkBhA7aIanYaikC/sGSmJ7l93QY1LftxXXwfEvlsU2YIgPKj2zw+FmbLGuGnPqr4A95SmbhAw8DgagUPx59rBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ff7ae9e6940db90bde9e863b14a055ba620cab5da438ab6aef0cd1e2ae948a8","last_reissued_at":"2026-07-05T09:26:36.723215Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:26:36.723215Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Molecular Fingerprints of Ice Surfaces in Sum Frequency Generation Spectra: a First Principles Machine Learning Study","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.soft"],"primary_cat":"physics.chem-ph","authors_text":"Davide Donadio, John T. Fourkas, Marcos F. Calegari Andrade, Margaret L. Berrens, Tuan Anh Pham","submitted_at":"2024-10-25T21:40:46Z","abstract_excerpt":"Understanding the molecular-level structure and dynamics of ice surfaces is crucial for deciphering several chemical, physical, and atmospheric processes. Vibrational sum-frequency generation (SFG) spectroscopy is the most prominent tool for probing the molecular-level structure of the air--ice interface as it is a surface-specific technique, but the molecular interpretation of SFG spectra is challenging. This study utilizes a machine-learning potential, along with dipole and polarizability models trained on ab initio data, to calculate the SFG spectrum of the air--ice interface. At temperatur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.19980","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/2410.19980/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":"2410.19980","created_at":"2026-07-05T09:26:36.723274+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.19980v1","created_at":"2026-07-05T09:26:36.723274+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.19980","created_at":"2026-07-05T09:26:36.723274+00:00"},{"alias_kind":"pith_short_12","alias_value":"R7325HTJIDNZ","created_at":"2026-07-05T09:26:36.723274+00:00"},{"alias_kind":"pith_short_16","alias_value":"R7325HTJIDNZBPPJ","created_at":"2026-07-05T09:26:36.723274+00:00"},{"alias_kind":"pith_short_8","alias_value":"R7325HTJ","created_at":"2026-07-05T09:26:36.723274+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/R7325HTJIDNZBPPJ5BR3CSQFLO","json":"https://pith.science/pith/R7325HTJIDNZBPPJ5BR3CSQFLO.json","graph_json":"https://pith.science/api/pith-number/R7325HTJIDNZBPPJ5BR3CSQFLO/graph.json","events_json":"https://pith.science/api/pith-number/R7325HTJIDNZBPPJ5BR3CSQFLO/events.json","paper":"https://pith.science/paper/R7325HTJ"},"agent_actions":{"view_html":"https://pith.science/pith/R7325HTJIDNZBPPJ5BR3CSQFLO","download_json":"https://pith.science/pith/R7325HTJIDNZBPPJ5BR3CSQFLO.json","view_paper":"https://pith.science/paper/R7325HTJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.19980&json=true","fetch_graph":"https://pith.science/api/pith-number/R7325HTJIDNZBPPJ5BR3CSQFLO/graph.json","fetch_events":"https://pith.science/api/pith-number/R7325HTJIDNZBPPJ5BR3CSQFLO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R7325HTJIDNZBPPJ5BR3CSQFLO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R7325HTJIDNZBPPJ5BR3CSQFLO/action/storage_attestation","attest_author":"https://pith.science/pith/R7325HTJIDNZBPPJ5BR3CSQFLO/action/author_attestation","sign_citation":"https://pith.science/pith/R7325HTJIDNZBPPJ5BR3CSQFLO/action/citation_signature","submit_replication":"https://pith.science/pith/R7325HTJIDNZBPPJ5BR3CSQFLO/action/replication_record"}},"created_at":"2026-07-05T09:26:36.723274+00:00","updated_at":"2026-07-05T09:26:36.723274+00:00"}