{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JOCISYKSM4TPY2CXCQMAK3AXDZ","short_pith_number":"pith:JOCISYKS","schema_version":"1.0","canonical_sha256":"4b848961526726fc68571418056c171e7cb95119709cc186b4c91b0dd3b4c854","source":{"kind":"arxiv","id":"2409.01931","version":2},"attestation_state":"computed","paper":{"title":"On the design space between molecular mechanics and machine learning force fields","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","physics.bio-ph","physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"Arnav Nagle, Daniel J. Cole, Joe G. Greener, John Z. H. Zhang, Joshua A. Rackers, Kenichiro Takaba, Kuang Yu, Kyunghyun Cho, Marcus Wieder, Mark E. Tuckerman, Michael S. Chen, Peter Eastman, Stefano Martiniani, Tong Zhu, Xinyan Wang, Yuanqing Wang, Yuzhi Xu","submitted_at":"2024-09-03T14:21:46Z","abstract_excerpt":"A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently enough and meaningfully enough to get quantitative insights, is among the most ardent dreams of biophysicists -- a dream, nevertheless, not to be fulfilled any time soon. Machine learning force fields (MLFFs) represent a meaningful endeavor towards this direction, where differentiable neural functions are parametrized to fit ab initio energies, and furthermore forces through automatic differentiation. We argue that, as of now, the utility of"},"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":"2409.01931","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2024-09-03T14:21:46Z","cross_cats_sorted":["cs.AI","cs.LG","physics.bio-ph","physics.comp-ph"],"title_canon_sha256":"fa13df8c8701c07367eb1c93a86f5a05e4bb869a7288ebc96421c0541ad8e1ec","abstract_canon_sha256":"59e3a31c00e02062b74214c5ef30bde76fac3e901c0096d39a27f9e2826e4552"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:44:15.883764Z","signature_b64":"boWG3PXad3mzMEMJaOHJIIFtj4M1WHs9K10j3jlmFN2L/IxbFWsaJQfsWyUoA4/hry91MJuk9l5DlMtRffLNDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b848961526726fc68571418056c171e7cb95119709cc186b4c91b0dd3b4c854","last_reissued_at":"2026-07-05T10:44:15.883276Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:44:15.883276Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the design space between molecular mechanics and machine learning force fields","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","physics.bio-ph","physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"Arnav Nagle, Daniel J. Cole, Joe G. Greener, John Z. H. Zhang, Joshua A. Rackers, Kenichiro Takaba, Kuang Yu, Kyunghyun Cho, Marcus Wieder, Mark E. Tuckerman, Michael S. Chen, Peter Eastman, Stefano Martiniani, Tong Zhu, Xinyan Wang, Yuanqing Wang, Yuzhi Xu","submitted_at":"2024-09-03T14:21:46Z","abstract_excerpt":"A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently enough and meaningfully enough to get quantitative insights, is among the most ardent dreams of biophysicists -- a dream, nevertheless, not to be fulfilled any time soon. Machine learning force fields (MLFFs) represent a meaningful endeavor towards this direction, where differentiable neural functions are parametrized to fit ab initio energies, and furthermore forces through automatic differentiation. We argue that, as of now, the utility of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.01931","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/2409.01931/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":"2409.01931","created_at":"2026-07-05T10:44:15.883340+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.01931v2","created_at":"2026-07-05T10:44:15.883340+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.01931","created_at":"2026-07-05T10:44:15.883340+00:00"},{"alias_kind":"pith_short_12","alias_value":"JOCISYKSM4TP","created_at":"2026-07-05T10:44:15.883340+00:00"},{"alias_kind":"pith_short_16","alias_value":"JOCISYKSM4TPY2CX","created_at":"2026-07-05T10:44:15.883340+00:00"},{"alias_kind":"pith_short_8","alias_value":"JOCISYKS","created_at":"2026-07-05T10:44:15.883340+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/JOCISYKSM4TPY2CXCQMAK3AXDZ","json":"https://pith.science/pith/JOCISYKSM4TPY2CXCQMAK3AXDZ.json","graph_json":"https://pith.science/api/pith-number/JOCISYKSM4TPY2CXCQMAK3AXDZ/graph.json","events_json":"https://pith.science/api/pith-number/JOCISYKSM4TPY2CXCQMAK3AXDZ/events.json","paper":"https://pith.science/paper/JOCISYKS"},"agent_actions":{"view_html":"https://pith.science/pith/JOCISYKSM4TPY2CXCQMAK3AXDZ","download_json":"https://pith.science/pith/JOCISYKSM4TPY2CXCQMAK3AXDZ.json","view_paper":"https://pith.science/paper/JOCISYKS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.01931&json=true","fetch_graph":"https://pith.science/api/pith-number/JOCISYKSM4TPY2CXCQMAK3AXDZ/graph.json","fetch_events":"https://pith.science/api/pith-number/JOCISYKSM4TPY2CXCQMAK3AXDZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JOCISYKSM4TPY2CXCQMAK3AXDZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JOCISYKSM4TPY2CXCQMAK3AXDZ/action/storage_attestation","attest_author":"https://pith.science/pith/JOCISYKSM4TPY2CXCQMAK3AXDZ/action/author_attestation","sign_citation":"https://pith.science/pith/JOCISYKSM4TPY2CXCQMAK3AXDZ/action/citation_signature","submit_replication":"https://pith.science/pith/JOCISYKSM4TPY2CXCQMAK3AXDZ/action/replication_record"}},"created_at":"2026-07-05T10:44:15.883340+00:00","updated_at":"2026-07-05T10:44:15.883340+00:00"}