{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MU7IU6MFMWEE4ITNXXC4AGFAVH","short_pith_number":"pith:MU7IU6MF","schema_version":"1.0","canonical_sha256":"653e8a798565884e226dbdc5c018a0a9fbf4465daeaf8867e56dd94de70f1eac","source":{"kind":"arxiv","id":"2506.15652","version":1},"attestation_state":"computed","paper":{"title":"A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Qimin Yan, Ting-Wei Hsu, Zhenyao Fang","submitted_at":"2025-06-18T17:29:59Z","abstract_excerpt":"Disorder, though naturally present in experimental samples and strongly influencing a wide range of material phenomena, remains underexplored in first-principles studies due to the computational cost of sampling the large supercell and configurational space. The recent development of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we introduce a computational framework that integrates GNNs with Monte Carlo simulations"},"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":"2506.15652","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2025-06-18T17:29:59Z","cross_cats_sorted":[],"title_canon_sha256":"6d88a184a4fef175b38cb3e50941731560d9671155dc939705432e32b7cae40e","abstract_canon_sha256":"27d9ae2ece8ac4981c321c69d1dfda9db8f16428667f2829eabcc2c1947213cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:23:47.821099Z","signature_b64":"6RWtfdAAMTij5qJOaMEA5NEqrh43MGsbA3CDTDuUtqmZsPHGU4YzTNDonYxAad/xHXaaXiOnppr7R8TrV6aQAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"653e8a798565884e226dbdc5c018a0a9fbf4465daeaf8867e56dd94de70f1eac","last_reissued_at":"2026-07-05T11:23:47.820573Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:23:47.820573Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Qimin Yan, Ting-Wei Hsu, Zhenyao Fang","submitted_at":"2025-06-18T17:29:59Z","abstract_excerpt":"Disorder, though naturally present in experimental samples and strongly influencing a wide range of material phenomena, remains underexplored in first-principles studies due to the computational cost of sampling the large supercell and configurational space. The recent development of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we introduce a computational framework that integrates GNNs with Monte Carlo simulations"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.15652","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/2506.15652/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":"2506.15652","created_at":"2026-07-05T11:23:47.820642+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.15652v1","created_at":"2026-07-05T11:23:47.820642+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.15652","created_at":"2026-07-05T11:23:47.820642+00:00"},{"alias_kind":"pith_short_12","alias_value":"MU7IU6MFMWEE","created_at":"2026-07-05T11:23:47.820642+00:00"},{"alias_kind":"pith_short_16","alias_value":"MU7IU6MFMWEE4ITN","created_at":"2026-07-05T11:23:47.820642+00:00"},{"alias_kind":"pith_short_8","alias_value":"MU7IU6MF","created_at":"2026-07-05T11:23:47.820642+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/MU7IU6MFMWEE4ITNXXC4AGFAVH","json":"https://pith.science/pith/MU7IU6MFMWEE4ITNXXC4AGFAVH.json","graph_json":"https://pith.science/api/pith-number/MU7IU6MFMWEE4ITNXXC4AGFAVH/graph.json","events_json":"https://pith.science/api/pith-number/MU7IU6MFMWEE4ITNXXC4AGFAVH/events.json","paper":"https://pith.science/paper/MU7IU6MF"},"agent_actions":{"view_html":"https://pith.science/pith/MU7IU6MFMWEE4ITNXXC4AGFAVH","download_json":"https://pith.science/pith/MU7IU6MFMWEE4ITNXXC4AGFAVH.json","view_paper":"https://pith.science/paper/MU7IU6MF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.15652&json=true","fetch_graph":"https://pith.science/api/pith-number/MU7IU6MFMWEE4ITNXXC4AGFAVH/graph.json","fetch_events":"https://pith.science/api/pith-number/MU7IU6MFMWEE4ITNXXC4AGFAVH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MU7IU6MFMWEE4ITNXXC4AGFAVH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MU7IU6MFMWEE4ITNXXC4AGFAVH/action/storage_attestation","attest_author":"https://pith.science/pith/MU7IU6MFMWEE4ITNXXC4AGFAVH/action/author_attestation","sign_citation":"https://pith.science/pith/MU7IU6MFMWEE4ITNXXC4AGFAVH/action/citation_signature","submit_replication":"https://pith.science/pith/MU7IU6MFMWEE4ITNXXC4AGFAVH/action/replication_record"}},"created_at":"2026-07-05T11:23:47.820642+00:00","updated_at":"2026-07-05T11:23:47.820642+00:00"}