{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OTLH5LNKHVIQDALGQYESREWTQO","short_pith_number":"pith:OTLH5LNK","schema_version":"1.0","canonical_sha256":"74d67eadaa3d5101816686092892d3838f0ae71580a3f08e1f0f3c59ca892c2a","source":{"kind":"arxiv","id":"2404.13694","version":1},"attestation_state":"computed","paper":{"title":"Solute segregation in polycrystalline aluminum from hybrid Monte Carlo and molecular dynamics simulations with a unified neuroevolution potential","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Jiahui Liu, Keke Song, Ping Qian, Shunda Chen, Yanjing Su, Zheyong Fan","submitted_at":"2024-04-21T15:49:12Z","abstract_excerpt":"One of the most effective methods to enhance the strength of aluminum alloys involves modifying grain boundaries (GBs) through solute segregation. However, the fundamental mechanisms of solute segregation and their impacts on material properties remain elusive. In this study, we implemented highly efficient hybrid Monte Carlo and molecular dynamics (MCMD) algorithms in the graphics process units molecular dynamics (GPUMD) package. Using this efficient MCMD approach combined with a general-purpose machine-learning-based neuroevolution potential (NEP) for 16 elemental metals and their alloys, we"},"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":"2404.13694","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-04-21T15:49:12Z","cross_cats_sorted":[],"title_canon_sha256":"bac11ab6436e49f86a4ee1a43bf252717d9289f0f1f3b407911b375b37f34ea6","abstract_canon_sha256":"3fa3b74c3b741a297d624afec5787223ece6a0e4669e8b9f9d9ed1c2e2685727"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:10:35.902495Z","signature_b64":"QT8VQ98oBCMvDXO/IjKQJz4mxy0jHtJdGQQsgQOpIrm4cabTTg3HqTGj8ZCTr3i6l8JBTpgBCBICM/BgeqZUBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"74d67eadaa3d5101816686092892d3838f0ae71580a3f08e1f0f3c59ca892c2a","last_reissued_at":"2026-07-05T08:10:35.902029Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:10:35.902029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Solute segregation in polycrystalline aluminum from hybrid Monte Carlo and molecular dynamics simulations with a unified neuroevolution potential","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Jiahui Liu, Keke Song, Ping Qian, Shunda Chen, Yanjing Su, Zheyong Fan","submitted_at":"2024-04-21T15:49:12Z","abstract_excerpt":"One of the most effective methods to enhance the strength of aluminum alloys involves modifying grain boundaries (GBs) through solute segregation. However, the fundamental mechanisms of solute segregation and their impacts on material properties remain elusive. In this study, we implemented highly efficient hybrid Monte Carlo and molecular dynamics (MCMD) algorithms in the graphics process units molecular dynamics (GPUMD) package. Using this efficient MCMD approach combined with a general-purpose machine-learning-based neuroevolution potential (NEP) for 16 elemental metals and their alloys, we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.13694","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/2404.13694/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":"2404.13694","created_at":"2026-07-05T08:10:35.902092+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.13694v1","created_at":"2026-07-05T08:10:35.902092+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.13694","created_at":"2026-07-05T08:10:35.902092+00:00"},{"alias_kind":"pith_short_12","alias_value":"OTLH5LNKHVIQ","created_at":"2026-07-05T08:10:35.902092+00:00"},{"alias_kind":"pith_short_16","alias_value":"OTLH5LNKHVIQDALG","created_at":"2026-07-05T08:10:35.902092+00:00"},{"alias_kind":"pith_short_8","alias_value":"OTLH5LNK","created_at":"2026-07-05T08:10:35.902092+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/OTLH5LNKHVIQDALGQYESREWTQO","json":"https://pith.science/pith/OTLH5LNKHVIQDALGQYESREWTQO.json","graph_json":"https://pith.science/api/pith-number/OTLH5LNKHVIQDALGQYESREWTQO/graph.json","events_json":"https://pith.science/api/pith-number/OTLH5LNKHVIQDALGQYESREWTQO/events.json","paper":"https://pith.science/paper/OTLH5LNK"},"agent_actions":{"view_html":"https://pith.science/pith/OTLH5LNKHVIQDALGQYESREWTQO","download_json":"https://pith.science/pith/OTLH5LNKHVIQDALGQYESREWTQO.json","view_paper":"https://pith.science/paper/OTLH5LNK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.13694&json=true","fetch_graph":"https://pith.science/api/pith-number/OTLH5LNKHVIQDALGQYESREWTQO/graph.json","fetch_events":"https://pith.science/api/pith-number/OTLH5LNKHVIQDALGQYESREWTQO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OTLH5LNKHVIQDALGQYESREWTQO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OTLH5LNKHVIQDALGQYESREWTQO/action/storage_attestation","attest_author":"https://pith.science/pith/OTLH5LNKHVIQDALGQYESREWTQO/action/author_attestation","sign_citation":"https://pith.science/pith/OTLH5LNKHVIQDALGQYESREWTQO/action/citation_signature","submit_replication":"https://pith.science/pith/OTLH5LNKHVIQDALGQYESREWTQO/action/replication_record"}},"created_at":"2026-07-05T08:10:35.902092+00:00","updated_at":"2026-07-05T08:10:35.902092+00:00"}