{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:I2Z6PUYSN6LR2MKEQQBCG2XL6N","short_pith_number":"pith:I2Z6PUYS","schema_version":"1.0","canonical_sha256":"46b3e7d3126f971d31448402236aebf34b1f5fcd9687cd0f0fc63f4547dd3b03","source":{"kind":"arxiv","id":"1812.00326","version":1},"attestation_state":"computed","paper":{"title":"Comparing optimization strategies for force field parameterization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"physics.comp-ph","authors_text":"Alper Kinaci, Badri Narayanan, Fatih G. Sen, Jeffrey Larson, Kiran Sasikumar, Maria K. Y. Chan, Michael J. Davis, Stefan M. Wild, Stephen K. Gray, Subramanian K. R. S. Sankaranarayanan","submitted_at":"2018-12-02T04:53:46Z","abstract_excerpt":"Classical molecular dynamics (MD) simulations enable modeling of materials and examination of microscopic details that are not accessible experimentally. The predictive capability of MD relies on the force field (FF) used to describe interatomic interactions. FF parameters are typically determined to reproduce selected material properties computed from density functional theory (DFT) and/or measured experimentally. A common practice in parameterizing FFs is to use least-squares local minimization algorithms. Genetic algorithms (GAs) have also been demonstrated as a viable global optimization a"},"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":"1812.00326","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-12-02T04:53:46Z","cross_cats_sorted":["cond-mat.mtrl-sci"],"title_canon_sha256":"b8942b84ec806fdf98d8ba2e2c2ba6b3f58b8f62847477e83a09adc3edda4771","abstract_canon_sha256":"e8abb1fab68fe841679939580b191522aa01e7ab260f4def654a11f99cb61df1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:21.605803Z","signature_b64":"vJ1oY5cpjzlhBbHeqcd7aRaSSkrd/jD/pqHk07YgsE5aVVB6GtrUstt7/zYxJ5RPb4CUdOjhG0XEBFeyrQ2sAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46b3e7d3126f971d31448402236aebf34b1f5fcd9687cd0f0fc63f4547dd3b03","last_reissued_at":"2026-05-17T23:59:21.605382Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:21.605382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Comparing optimization strategies for force field parameterization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"physics.comp-ph","authors_text":"Alper Kinaci, Badri Narayanan, Fatih G. Sen, Jeffrey Larson, Kiran Sasikumar, Maria K. Y. Chan, Michael J. Davis, Stefan M. Wild, Stephen K. Gray, Subramanian K. R. S. Sankaranarayanan","submitted_at":"2018-12-02T04:53:46Z","abstract_excerpt":"Classical molecular dynamics (MD) simulations enable modeling of materials and examination of microscopic details that are not accessible experimentally. The predictive capability of MD relies on the force field (FF) used to describe interatomic interactions. FF parameters are typically determined to reproduce selected material properties computed from density functional theory (DFT) and/or measured experimentally. A common practice in parameterizing FFs is to use least-squares local minimization algorithms. Genetic algorithms (GAs) have also been demonstrated as a viable global optimization a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.00326","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":""},"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":"1812.00326","created_at":"2026-05-17T23:59:21.605438+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.00326v1","created_at":"2026-05-17T23:59:21.605438+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.00326","created_at":"2026-05-17T23:59:21.605438+00:00"},{"alias_kind":"pith_short_12","alias_value":"I2Z6PUYSN6LR","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"I2Z6PUYSN6LR2MKE","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"I2Z6PUYS","created_at":"2026-05-18T12:32:28.185984+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/I2Z6PUYSN6LR2MKEQQBCG2XL6N","json":"https://pith.science/pith/I2Z6PUYSN6LR2MKEQQBCG2XL6N.json","graph_json":"https://pith.science/api/pith-number/I2Z6PUYSN6LR2MKEQQBCG2XL6N/graph.json","events_json":"https://pith.science/api/pith-number/I2Z6PUYSN6LR2MKEQQBCG2XL6N/events.json","paper":"https://pith.science/paper/I2Z6PUYS"},"agent_actions":{"view_html":"https://pith.science/pith/I2Z6PUYSN6LR2MKEQQBCG2XL6N","download_json":"https://pith.science/pith/I2Z6PUYSN6LR2MKEQQBCG2XL6N.json","view_paper":"https://pith.science/paper/I2Z6PUYS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.00326&json=true","fetch_graph":"https://pith.science/api/pith-number/I2Z6PUYSN6LR2MKEQQBCG2XL6N/graph.json","fetch_events":"https://pith.science/api/pith-number/I2Z6PUYSN6LR2MKEQQBCG2XL6N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I2Z6PUYSN6LR2MKEQQBCG2XL6N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I2Z6PUYSN6LR2MKEQQBCG2XL6N/action/storage_attestation","attest_author":"https://pith.science/pith/I2Z6PUYSN6LR2MKEQQBCG2XL6N/action/author_attestation","sign_citation":"https://pith.science/pith/I2Z6PUYSN6LR2MKEQQBCG2XL6N/action/citation_signature","submit_replication":"https://pith.science/pith/I2Z6PUYSN6LR2MKEQQBCG2XL6N/action/replication_record"}},"created_at":"2026-05-17T23:59:21.605438+00:00","updated_at":"2026-05-17T23:59:21.605438+00:00"}