{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WLRUAWGKYMUZ35WMKKRSPF6JZI","short_pith_number":"pith:WLRUAWGK","schema_version":"1.0","canonical_sha256":"b2e34058cac3299df6cc52a32797c9ca1d92f879c76e461befb4e5de925dcaf8","source":{"kind":"arxiv","id":"2605.30012","version":1},"attestation_state":"computed","paper":{"title":"Charting the thermodynamic stability of hybrid perovskite alloys with machine learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Armi Tiihonen, Jarno Laakso, Patrick Rinke","submitted_at":"2026-05-28T14:38:31Z","abstract_excerpt":"Alloy-based perovskite solar cells offer tunable properties and improved stability, but their complexity has impeded accurate modeling, hindering development. We present a machine-learning (ML) accelerated atomistic modeling approach for the phase stability of (Cs/FA)Pb(Br/I)3 and (Cs/FA)Sn(Br/I)3 perovskites, with FA being formamidinium. To make such quaternary alloys tractable, we adopt a two-level ML strategy, combining 1) graph neural network interatomic potentials trained on density functional theory data for efficient structure relaxations with 2) secondary ML models for direct energy pr"},"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":"2605.30012","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-28T14:38:31Z","cross_cats_sorted":[],"title_canon_sha256":"39c00214fb263d961fda419efcd595dbefd74057a72249eca660a5d827918610","abstract_canon_sha256":"79a21a2c59e824f560cb0e31a9832c79e46f1fcf2d358a1d517910f59dbe24c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T02:06:06.902767Z","signature_b64":"1DhqvEC/H/xgU45uEYuxnj+v6H9RRVWb+yl4dExLuaEHJTAhpYmVtghLkgJsEW9frHfjYyZOxDjxXEushywcAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2e34058cac3299df6cc52a32797c9ca1d92f879c76e461befb4e5de925dcaf8","last_reissued_at":"2026-05-29T02:06:06.901914Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T02:06:06.901914Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Charting the thermodynamic stability of hybrid perovskite alloys with machine learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Armi Tiihonen, Jarno Laakso, Patrick Rinke","submitted_at":"2026-05-28T14:38:31Z","abstract_excerpt":"Alloy-based perovskite solar cells offer tunable properties and improved stability, but their complexity has impeded accurate modeling, hindering development. We present a machine-learning (ML) accelerated atomistic modeling approach for the phase stability of (Cs/FA)Pb(Br/I)3 and (Cs/FA)Sn(Br/I)3 perovskites, with FA being formamidinium. To make such quaternary alloys tractable, we adopt a two-level ML strategy, combining 1) graph neural network interatomic potentials trained on density functional theory data for efficient structure relaxations with 2) secondary ML models for direct energy pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30012","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/2605.30012/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":"2605.30012","created_at":"2026-05-29T02:06:06.902033+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.30012v1","created_at":"2026-05-29T02:06:06.902033+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30012","created_at":"2026-05-29T02:06:06.902033+00:00"},{"alias_kind":"pith_short_12","alias_value":"WLRUAWGKYMUZ","created_at":"2026-05-29T02:06:06.902033+00:00"},{"alias_kind":"pith_short_16","alias_value":"WLRUAWGKYMUZ35WM","created_at":"2026-05-29T02:06:06.902033+00:00"},{"alias_kind":"pith_short_8","alias_value":"WLRUAWGK","created_at":"2026-05-29T02:06:06.902033+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/WLRUAWGKYMUZ35WMKKRSPF6JZI","json":"https://pith.science/pith/WLRUAWGKYMUZ35WMKKRSPF6JZI.json","graph_json":"https://pith.science/api/pith-number/WLRUAWGKYMUZ35WMKKRSPF6JZI/graph.json","events_json":"https://pith.science/api/pith-number/WLRUAWGKYMUZ35WMKKRSPF6JZI/events.json","paper":"https://pith.science/paper/WLRUAWGK"},"agent_actions":{"view_html":"https://pith.science/pith/WLRUAWGKYMUZ35WMKKRSPF6JZI","download_json":"https://pith.science/pith/WLRUAWGKYMUZ35WMKKRSPF6JZI.json","view_paper":"https://pith.science/paper/WLRUAWGK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.30012&json=true","fetch_graph":"https://pith.science/api/pith-number/WLRUAWGKYMUZ35WMKKRSPF6JZI/graph.json","fetch_events":"https://pith.science/api/pith-number/WLRUAWGKYMUZ35WMKKRSPF6JZI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WLRUAWGKYMUZ35WMKKRSPF6JZI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WLRUAWGKYMUZ35WMKKRSPF6JZI/action/storage_attestation","attest_author":"https://pith.science/pith/WLRUAWGKYMUZ35WMKKRSPF6JZI/action/author_attestation","sign_citation":"https://pith.science/pith/WLRUAWGKYMUZ35WMKKRSPF6JZI/action/citation_signature","submit_replication":"https://pith.science/pith/WLRUAWGKYMUZ35WMKKRSPF6JZI/action/replication_record"}},"created_at":"2026-05-29T02:06:06.902033+00:00","updated_at":"2026-05-29T02:06:06.902033+00:00"}