{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SC23DYPSKYAMBU4DIXHTPAFE46","short_pith_number":"pith:SC23DYPS","schema_version":"1.0","canonical_sha256":"90b5b1e1f25600c0d38345cf3780a4e7aaa1eb967bc0e1a1fdce97116d4ae9ec","source":{"kind":"arxiv","id":"2505.06462","version":2},"attestation_state":"computed","paper":{"title":"Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"physics.chem-ph","authors_text":"Aidan A. Fike, Biswajit Santra, Garvit Agarwal, James Stevenson, John L. Weber, Karl Leswing, Leif D. Jacobson, Mathew D. Halls, Richard A. Friesner, Rishabh D. Guha, Robert Abel, Xiaowei Xie, Yujing Wei","submitted_at":"2025-05-09T23:06:55Z","abstract_excerpt":"Machine learning force fields (MLFFs) have emerged as a sophisticated tool for cost-efficient atomistic simulations approaching DFT accuracy, with recent message passing MLFFs able to cover the entire periodic table. We present an invariant message passing MLFF architecture (MPNICE) which iteratively predicts atomic partial charges, including long-range interactions, enabling the prediction of charge-dependent properties while achieving 5-20x faster inference versus models with comparable accuracy. We train direct and delta-learned MPNICE models for organic systems, and benchmark against exper"},"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":"2505.06462","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2025-05-09T23:06:55Z","cross_cats_sorted":["cond-mat.mtrl-sci"],"title_canon_sha256":"75789e2a71b83f1e5671703ca3a9e649d7b04ddd532b45048646f2beb6af3241","abstract_canon_sha256":"78f188899dc4b4dcd7296d9983607422a5916ada1eb4a019add1992197e2c611"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:47:22.810064Z","signature_b64":"GAoEBfG3ep4undP7uCaVulIeN0UZU/dc0M6h8P6ajCNJ3F14H/a03c4dh8kAhgpYbNxsiCMIZAljcKGwbA8mDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90b5b1e1f25600c0d38345cf3780a4e7aaa1eb967bc0e1a1fdce97116d4ae9ec","last_reissued_at":"2026-07-05T11:47:22.809519Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:47:22.809519Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"physics.chem-ph","authors_text":"Aidan A. Fike, Biswajit Santra, Garvit Agarwal, James Stevenson, John L. Weber, Karl Leswing, Leif D. Jacobson, Mathew D. Halls, Richard A. Friesner, Rishabh D. Guha, Robert Abel, Xiaowei Xie, Yujing Wei","submitted_at":"2025-05-09T23:06:55Z","abstract_excerpt":"Machine learning force fields (MLFFs) have emerged as a sophisticated tool for cost-efficient atomistic simulations approaching DFT accuracy, with recent message passing MLFFs able to cover the entire periodic table. We present an invariant message passing MLFF architecture (MPNICE) which iteratively predicts atomic partial charges, including long-range interactions, enabling the prediction of charge-dependent properties while achieving 5-20x faster inference versus models with comparable accuracy. We train direct and delta-learned MPNICE models for organic systems, and benchmark against exper"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.06462","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/2505.06462/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":"2505.06462","created_at":"2026-07-05T11:47:22.809573+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.06462v2","created_at":"2026-07-05T11:47:22.809573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.06462","created_at":"2026-07-05T11:47:22.809573+00:00"},{"alias_kind":"pith_short_12","alias_value":"SC23DYPSKYAM","created_at":"2026-07-05T11:47:22.809573+00:00"},{"alias_kind":"pith_short_16","alias_value":"SC23DYPSKYAMBU4D","created_at":"2026-07-05T11:47:22.809573+00:00"},{"alias_kind":"pith_short_8","alias_value":"SC23DYPS","created_at":"2026-07-05T11:47:22.809573+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/SC23DYPSKYAMBU4DIXHTPAFE46","json":"https://pith.science/pith/SC23DYPSKYAMBU4DIXHTPAFE46.json","graph_json":"https://pith.science/api/pith-number/SC23DYPSKYAMBU4DIXHTPAFE46/graph.json","events_json":"https://pith.science/api/pith-number/SC23DYPSKYAMBU4DIXHTPAFE46/events.json","paper":"https://pith.science/paper/SC23DYPS"},"agent_actions":{"view_html":"https://pith.science/pith/SC23DYPSKYAMBU4DIXHTPAFE46","download_json":"https://pith.science/pith/SC23DYPSKYAMBU4DIXHTPAFE46.json","view_paper":"https://pith.science/paper/SC23DYPS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.06462&json=true","fetch_graph":"https://pith.science/api/pith-number/SC23DYPSKYAMBU4DIXHTPAFE46/graph.json","fetch_events":"https://pith.science/api/pith-number/SC23DYPSKYAMBU4DIXHTPAFE46/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SC23DYPSKYAMBU4DIXHTPAFE46/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SC23DYPSKYAMBU4DIXHTPAFE46/action/storage_attestation","attest_author":"https://pith.science/pith/SC23DYPSKYAMBU4DIXHTPAFE46/action/author_attestation","sign_citation":"https://pith.science/pith/SC23DYPSKYAMBU4DIXHTPAFE46/action/citation_signature","submit_replication":"https://pith.science/pith/SC23DYPSKYAMBU4DIXHTPAFE46/action/replication_record"}},"created_at":"2026-07-05T11:47:22.809573+00:00","updated_at":"2026-07-05T11:47:22.809573+00:00"}