{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:H3GFUT4VK57S3NHQ6DI3YG2ICD","short_pith_number":"pith:H3GFUT4V","schema_version":"1.0","canonical_sha256":"3ecc5a4f95577f2db4f0f0d1bc1b4810eacfe37d5484cbf79441e920af9b4e68","source":{"kind":"arxiv","id":"2408.13416","version":1},"attestation_state":"computed","paper":{"title":"Accelerating material melting temperature predictions by implementing machine learning potentials in the SLUSCHI package","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Audrey CampBell, Ligen Wang, Qi-Jun Hong","submitted_at":"2024-08-24T00:51:04Z","abstract_excerpt":"The SLUSCHI (Solid and Liquid in Ultra Small Coexistence with Hovering Interfaces) automated package, with interface to the first-principles code VASP (Vienna Ab initio Simulation Package), was developed by us for efficiently determining the melting temperatures of various materials. However, performing many molecular dynamics simulations for small liquid-solid coexisting supercells to predict the melting temperature of a material is still computationally expensive, often requiring weeks and tens to hundreds of thousands of CPU hours to complete. In the present paper, we made an attempt to int"},"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":"2408.13416","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-08-24T00:51:04Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"ce3d9ffa3ba824d75b201c6b7e6e05bee977e0eefc9d97a2dd83060602c48b7e","abstract_canon_sha256":"68bf1c6a31fe02056f04549453d6d85cba7d9d3e3422895bf5f7e49602c56a52"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:58:50.186632Z","signature_b64":"AU6ZJbocH8i/Y5F9zEd6ygtiW9BLuBtpQFz/f8Zy/hifvqNW+b8Fm8JqfHvDyvfq5ihnDir2yVnpS/cbFV3MAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ecc5a4f95577f2db4f0f0d1bc1b4810eacfe37d5484cbf79441e920af9b4e68","last_reissued_at":"2026-07-05T08:58:50.186124Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:58:50.186124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accelerating material melting temperature predictions by implementing machine learning potentials in the SLUSCHI package","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Audrey CampBell, Ligen Wang, Qi-Jun Hong","submitted_at":"2024-08-24T00:51:04Z","abstract_excerpt":"The SLUSCHI (Solid and Liquid in Ultra Small Coexistence with Hovering Interfaces) automated package, with interface to the first-principles code VASP (Vienna Ab initio Simulation Package), was developed by us for efficiently determining the melting temperatures of various materials. However, performing many molecular dynamics simulations for small liquid-solid coexisting supercells to predict the melting temperature of a material is still computationally expensive, often requiring weeks and tens to hundreds of thousands of CPU hours to complete. In the present paper, we made an attempt to int"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.13416","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/2408.13416/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":"2408.13416","created_at":"2026-07-05T08:58:50.186189+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.13416v1","created_at":"2026-07-05T08:58:50.186189+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.13416","created_at":"2026-07-05T08:58:50.186189+00:00"},{"alias_kind":"pith_short_12","alias_value":"H3GFUT4VK57S","created_at":"2026-07-05T08:58:50.186189+00:00"},{"alias_kind":"pith_short_16","alias_value":"H3GFUT4VK57S3NHQ","created_at":"2026-07-05T08:58:50.186189+00:00"},{"alias_kind":"pith_short_8","alias_value":"H3GFUT4V","created_at":"2026-07-05T08:58:50.186189+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/H3GFUT4VK57S3NHQ6DI3YG2ICD","json":"https://pith.science/pith/H3GFUT4VK57S3NHQ6DI3YG2ICD.json","graph_json":"https://pith.science/api/pith-number/H3GFUT4VK57S3NHQ6DI3YG2ICD/graph.json","events_json":"https://pith.science/api/pith-number/H3GFUT4VK57S3NHQ6DI3YG2ICD/events.json","paper":"https://pith.science/paper/H3GFUT4V"},"agent_actions":{"view_html":"https://pith.science/pith/H3GFUT4VK57S3NHQ6DI3YG2ICD","download_json":"https://pith.science/pith/H3GFUT4VK57S3NHQ6DI3YG2ICD.json","view_paper":"https://pith.science/paper/H3GFUT4V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.13416&json=true","fetch_graph":"https://pith.science/api/pith-number/H3GFUT4VK57S3NHQ6DI3YG2ICD/graph.json","fetch_events":"https://pith.science/api/pith-number/H3GFUT4VK57S3NHQ6DI3YG2ICD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H3GFUT4VK57S3NHQ6DI3YG2ICD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H3GFUT4VK57S3NHQ6DI3YG2ICD/action/storage_attestation","attest_author":"https://pith.science/pith/H3GFUT4VK57S3NHQ6DI3YG2ICD/action/author_attestation","sign_citation":"https://pith.science/pith/H3GFUT4VK57S3NHQ6DI3YG2ICD/action/citation_signature","submit_replication":"https://pith.science/pith/H3GFUT4VK57S3NHQ6DI3YG2ICD/action/replication_record"}},"created_at":"2026-07-05T08:58:50.186189+00:00","updated_at":"2026-07-05T08:58:50.186189+00:00"}