{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ANVJRRAZOO5PG2JHJQ35SRDCY2","short_pith_number":"pith:ANVJRRAZ","schema_version":"1.0","canonical_sha256":"036a98c41973baf369274c37d94462c683bfc50397101595d3821c945b4b2481","source":{"kind":"arxiv","id":"2502.12870","version":1},"attestation_state":"computed","paper":{"title":"Transferable Machine Learning Potential X-MACE for Excited States using Integrated DeepSets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.chem-ph","authors_text":"Christoph Ortner, Julia Westermayr, Rhyan Barrett","submitted_at":"2025-02-18T14:02:43Z","abstract_excerpt":"Conical intersections serve as critical gateways in photochemical reactions, enabling rapid nonradiative transitions between potential energy surfaces that underpin fundamental processes such as photosynthesis or vision. Their calculation with quantum chemistry is, however, extremely computationally intensive and their modeling with machine learning poses a significant challenge due to their inherently non-smooth and complex nature. To address this challenge, we introduce a deep learning architecture designed to precisely model excited states and improve their accuracy around these critical, n"},"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":"2502.12870","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2025-02-18T14:02:43Z","cross_cats_sorted":[],"title_canon_sha256":"370b405b75a810c3cf7fd434866e9e5c8f5a93d20eda6d83d5ffdfd4b34c43b4","abstract_canon_sha256":"c80db8e30107afd17f48670d49ea9e0d1a8c8092dad18421ec65ddce98dda24d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:16:25.650034Z","signature_b64":"KgU2UiDhx3ZGgJS/bNJWV1llb1fpzbFsJ2Qa+P9U0zqle/2P6C+nFq5rfW1CZkeN1m/B9b5/iYr0VgKbGAk+CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"036a98c41973baf369274c37d94462c683bfc50397101595d3821c945b4b2481","last_reissued_at":"2026-07-05T10:16:25.649459Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:16:25.649459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transferable Machine Learning Potential X-MACE for Excited States using Integrated DeepSets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.chem-ph","authors_text":"Christoph Ortner, Julia Westermayr, Rhyan Barrett","submitted_at":"2025-02-18T14:02:43Z","abstract_excerpt":"Conical intersections serve as critical gateways in photochemical reactions, enabling rapid nonradiative transitions between potential energy surfaces that underpin fundamental processes such as photosynthesis or vision. Their calculation with quantum chemistry is, however, extremely computationally intensive and their modeling with machine learning poses a significant challenge due to their inherently non-smooth and complex nature. To address this challenge, we introduce a deep learning architecture designed to precisely model excited states and improve their accuracy around these critical, n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.12870","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/2502.12870/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":"2502.12870","created_at":"2026-07-05T10:16:25.649531+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.12870v1","created_at":"2026-07-05T10:16:25.649531+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.12870","created_at":"2026-07-05T10:16:25.649531+00:00"},{"alias_kind":"pith_short_12","alias_value":"ANVJRRAZOO5P","created_at":"2026-07-05T10:16:25.649531+00:00"},{"alias_kind":"pith_short_16","alias_value":"ANVJRRAZOO5PG2JH","created_at":"2026-07-05T10:16:25.649531+00:00"},{"alias_kind":"pith_short_8","alias_value":"ANVJRRAZ","created_at":"2026-07-05T10:16:25.649531+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.20358","citing_title":"Modeling phase separation in polymer-derived silicon carbonitride ceramics through extended machine learning molecular dynamics","ref_index":60,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20358","citing_title":"Modeling phase separation in polymer-derived silicon carbonitride ceramics through extended machine learning molecular dynamics","ref_index":60,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ANVJRRAZOO5PG2JHJQ35SRDCY2","json":"https://pith.science/pith/ANVJRRAZOO5PG2JHJQ35SRDCY2.json","graph_json":"https://pith.science/api/pith-number/ANVJRRAZOO5PG2JHJQ35SRDCY2/graph.json","events_json":"https://pith.science/api/pith-number/ANVJRRAZOO5PG2JHJQ35SRDCY2/events.json","paper":"https://pith.science/paper/ANVJRRAZ"},"agent_actions":{"view_html":"https://pith.science/pith/ANVJRRAZOO5PG2JHJQ35SRDCY2","download_json":"https://pith.science/pith/ANVJRRAZOO5PG2JHJQ35SRDCY2.json","view_paper":"https://pith.science/paper/ANVJRRAZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.12870&json=true","fetch_graph":"https://pith.science/api/pith-number/ANVJRRAZOO5PG2JHJQ35SRDCY2/graph.json","fetch_events":"https://pith.science/api/pith-number/ANVJRRAZOO5PG2JHJQ35SRDCY2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ANVJRRAZOO5PG2JHJQ35SRDCY2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ANVJRRAZOO5PG2JHJQ35SRDCY2/action/storage_attestation","attest_author":"https://pith.science/pith/ANVJRRAZOO5PG2JHJQ35SRDCY2/action/author_attestation","sign_citation":"https://pith.science/pith/ANVJRRAZOO5PG2JHJQ35SRDCY2/action/citation_signature","submit_replication":"https://pith.science/pith/ANVJRRAZOO5PG2JHJQ35SRDCY2/action/replication_record"}},"created_at":"2026-07-05T10:16:25.649531+00:00","updated_at":"2026-07-05T10:16:25.649531+00:00"}