{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DPYASB7FGPELBUGYLKBIX3BLPG","short_pith_number":"pith:DPYASB7F","schema_version":"1.0","canonical_sha256":"1bf00907e533c8b0d0d85a828bec2b79a6720a60e5d0b3ace32f0b4f9826e308","source":{"kind":"arxiv","id":"2312.17596","version":2},"attestation_state":"computed","paper":{"title":"A \"Magnetic\" Machine Learning Interatomic Potential for Nickel","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"A. S. L. Subrahmanyam Pattamatta, David J. Srolovitz, Tongqi Wen, Xiaoguo Gong, Zhuoyuan Li","submitted_at":"2023-12-29T13:18:50Z","abstract_excerpt":"Nickel (Ni) is a magnetic transition metal with two allotropic phases, stable face-centered cubic (FCC) and metastable hexagonal close-packed (HCP), widely used in structural applications. Magnetism affects many mechanical and defect properties, but spin-polarized density functional theory (DFT) calculations are computationally inefficient for studying material behavior requiring large system sizes and/or long simulation times. Here we develop a \"magnetism-hidden\" machine-learning Deep Potential (DP) model for Ni without a descriptor for magnetic moments, using training datasets derived from s"},"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":"2312.17596","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2023-12-29T13:18:50Z","cross_cats_sorted":[],"title_canon_sha256":"d7bd13f2e170d9c690574e0238ecee9f8161013555439fc9c45dacecfd8b4c74","abstract_canon_sha256":"f5b1fc8d7acee07ed4fc387d3985e096583d74ff25ec946180bab5b3ccf911e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:56:58.841171Z","signature_b64":"p2wenAjq33xJ0UVMiGu6e1fTEQkZxY0lypryQastPOdOa9Iz551gTS2RVHwbnsPxbOvT3sWDVXyBwO7EuO/sAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1bf00907e533c8b0d0d85a828bec2b79a6720a60e5d0b3ace32f0b4f9826e308","last_reissued_at":"2026-07-05T08:56:58.840747Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:56:58.840747Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A \"Magnetic\" Machine Learning Interatomic Potential for Nickel","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"A. S. L. Subrahmanyam Pattamatta, David J. Srolovitz, Tongqi Wen, Xiaoguo Gong, Zhuoyuan Li","submitted_at":"2023-12-29T13:18:50Z","abstract_excerpt":"Nickel (Ni) is a magnetic transition metal with two allotropic phases, stable face-centered cubic (FCC) and metastable hexagonal close-packed (HCP), widely used in structural applications. Magnetism affects many mechanical and defect properties, but spin-polarized density functional theory (DFT) calculations are computationally inefficient for studying material behavior requiring large system sizes and/or long simulation times. Here we develop a \"magnetism-hidden\" machine-learning Deep Potential (DP) model for Ni without a descriptor for magnetic moments, using training datasets derived from s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.17596","kind":"arxiv","version":2},"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/2312.17596/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":"2312.17596","created_at":"2026-07-05T08:56:58.840804+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.17596v2","created_at":"2026-07-05T08:56:58.840804+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.17596","created_at":"2026-07-05T08:56:58.840804+00:00"},{"alias_kind":"pith_short_12","alias_value":"DPYASB7FGPEL","created_at":"2026-07-05T08:56:58.840804+00:00"},{"alias_kind":"pith_short_16","alias_value":"DPYASB7FGPELBUGY","created_at":"2026-07-05T08:56:58.840804+00:00"},{"alias_kind":"pith_short_8","alias_value":"DPYASB7F","created_at":"2026-07-05T08:56:58.840804+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/DPYASB7FGPELBUGYLKBIX3BLPG","json":"https://pith.science/pith/DPYASB7FGPELBUGYLKBIX3BLPG.json","graph_json":"https://pith.science/api/pith-number/DPYASB7FGPELBUGYLKBIX3BLPG/graph.json","events_json":"https://pith.science/api/pith-number/DPYASB7FGPELBUGYLKBIX3BLPG/events.json","paper":"https://pith.science/paper/DPYASB7F"},"agent_actions":{"view_html":"https://pith.science/pith/DPYASB7FGPELBUGYLKBIX3BLPG","download_json":"https://pith.science/pith/DPYASB7FGPELBUGYLKBIX3BLPG.json","view_paper":"https://pith.science/paper/DPYASB7F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.17596&json=true","fetch_graph":"https://pith.science/api/pith-number/DPYASB7FGPELBUGYLKBIX3BLPG/graph.json","fetch_events":"https://pith.science/api/pith-number/DPYASB7FGPELBUGYLKBIX3BLPG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DPYASB7FGPELBUGYLKBIX3BLPG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DPYASB7FGPELBUGYLKBIX3BLPG/action/storage_attestation","attest_author":"https://pith.science/pith/DPYASB7FGPELBUGYLKBIX3BLPG/action/author_attestation","sign_citation":"https://pith.science/pith/DPYASB7FGPELBUGYLKBIX3BLPG/action/citation_signature","submit_replication":"https://pith.science/pith/DPYASB7FGPELBUGYLKBIX3BLPG/action/replication_record"}},"created_at":"2026-07-05T08:56:58.840804+00:00","updated_at":"2026-07-05T08:56:58.840804+00:00"}