{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PQJMQLTETYUMIAISVVI6BNJ2LH","short_pith_number":"pith:PQJMQLTE","schema_version":"1.0","canonical_sha256":"7c12c82e649e28c40112ad51e0b53a59cfa87eacd4b085f7296e2e275bdc7660","source":{"kind":"arxiv","id":"1805.09003","version":2},"attestation_state":"computed","paper":{"title":"End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","physics.chem-ph"],"primary_cat":"physics.comp-ph","authors_text":"Han Wang, Jiequn Han, Linfeng Zhang, Roberto Car, Weinan E, Wissam A. Saidi","submitted_at":"2018-05-23T08:11:20Z","abstract_excerpt":"Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES for a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserve"},"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":"1805.09003","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-05-23T08:11:20Z","cross_cats_sorted":["cond-mat.mtrl-sci","physics.chem-ph"],"title_canon_sha256":"ba877be0036b640ca5b986da513e9aef185767f6a249158ace13f8e1b3f4807a","abstract_canon_sha256":"f4e49b44040021063cf6d02ce3db5d42900cb250c8dc5cf4b91e6d10d14b5e79"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:20:09.012018Z","signature_b64":"BHAX0MpbZsrTqKcV1redGRrdaBJM5J2z9Kizp+yOphbEuwt/UIcyLl4TYA7V5WYyXw4ZfqCVEoCkpqsfF+RQCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c12c82e649e28c40112ad51e0b53a59cfa87eacd4b085f7296e2e275bdc7660","last_reissued_at":"2026-07-05T01:20:09.011524Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:20:09.011524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","physics.chem-ph"],"primary_cat":"physics.comp-ph","authors_text":"Han Wang, Jiequn Han, Linfeng Zhang, Roberto Car, Weinan E, Wissam A. Saidi","submitted_at":"2018-05-23T08:11:20Z","abstract_excerpt":"Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES for a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.09003","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/1805.09003/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":"1805.09003","created_at":"2026-07-05T01:20:09.011581+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.09003v2","created_at":"2026-07-05T01:20:09.011581+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.09003","created_at":"2026-07-05T01:20:09.011581+00:00"},{"alias_kind":"pith_short_12","alias_value":"PQJMQLTETYUM","created_at":"2026-07-05T01:20:09.011581+00:00"},{"alias_kind":"pith_short_16","alias_value":"PQJMQLTETYUMIAIS","created_at":"2026-07-05T01:20:09.011581+00:00"},{"alias_kind":"pith_short_8","alias_value":"PQJMQLTE","created_at":"2026-07-05T01:20:09.011581+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.03046","citing_title":"Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing","ref_index":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PQJMQLTETYUMIAISVVI6BNJ2LH","json":"https://pith.science/pith/PQJMQLTETYUMIAISVVI6BNJ2LH.json","graph_json":"https://pith.science/api/pith-number/PQJMQLTETYUMIAISVVI6BNJ2LH/graph.json","events_json":"https://pith.science/api/pith-number/PQJMQLTETYUMIAISVVI6BNJ2LH/events.json","paper":"https://pith.science/paper/PQJMQLTE"},"agent_actions":{"view_html":"https://pith.science/pith/PQJMQLTETYUMIAISVVI6BNJ2LH","download_json":"https://pith.science/pith/PQJMQLTETYUMIAISVVI6BNJ2LH.json","view_paper":"https://pith.science/paper/PQJMQLTE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.09003&json=true","fetch_graph":"https://pith.science/api/pith-number/PQJMQLTETYUMIAISVVI6BNJ2LH/graph.json","fetch_events":"https://pith.science/api/pith-number/PQJMQLTETYUMIAISVVI6BNJ2LH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PQJMQLTETYUMIAISVVI6BNJ2LH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PQJMQLTETYUMIAISVVI6BNJ2LH/action/storage_attestation","attest_author":"https://pith.science/pith/PQJMQLTETYUMIAISVVI6BNJ2LH/action/author_attestation","sign_citation":"https://pith.science/pith/PQJMQLTETYUMIAISVVI6BNJ2LH/action/citation_signature","submit_replication":"https://pith.science/pith/PQJMQLTETYUMIAISVVI6BNJ2LH/action/replication_record"}},"created_at":"2026-07-05T01:20:09.011581+00:00","updated_at":"2026-07-05T01:20:09.011581+00:00"}