{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:PNPSR7CCA5KZNX2CSJD5RZKGOV","short_pith_number":"pith:PNPSR7CC","schema_version":"1.0","canonical_sha256":"7b5f28fc42075596df429247d8e5467575a575715b062d06f7b8835205c390c0","source":{"kind":"arxiv","id":"2204.13295","version":1},"attestation_state":"computed","paper":{"title":"A novel machine learning enabled hybrid optimization framework for efficient and transferable coarse-graining of a model polymer","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"Adrian Menzel, Andrew W Phillips, Elnaz Hajizadeh, Hansani Weeratunge, Kate Smith-Miles, Ronald G Larson, Zakiya Shireen","submitted_at":"2022-04-28T05:43:50Z","abstract_excerpt":"This work presents a novel framework governing the development of an efficient, accurate, and transferable coarse-grained (CG) model of a polyether material. The proposed framework combines the two fundamentally different classical optimization approaches for the development of coarse-grained model parameters; namely bottom-up and top-down approaches. This is achieved through integrating the optimization algorithms into a machine learning (ML) model, trained using molecular dynamics (MD) simulation data. In the bottom-up approach, bonded interactions of the CG model are optimized using deep ne"},"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":"2204.13295","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2022-04-28T05:43:50Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"e33b9dc576aa63a5c79b416e2e6682ef7e95caa8fa7085154141a5662595d30c","abstract_canon_sha256":"d0ae522db964cadeca193796c7c49bf962890eb721fbf453e4e3e1366c07fd6e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:18:33.336785Z","signature_b64":"RQUQHJnQBQiexGtyg5tE6Eg9oXdM00q2T1Mx4pkgqFNaxbg+N3vn4u8PR3jnowpav1fmdpC7gRc42k9sxOA3DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7b5f28fc42075596df429247d8e5467575a575715b062d06f7b8835205c390c0","last_reissued_at":"2026-07-05T04:18:33.336370Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:18:33.336370Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A novel machine learning enabled hybrid optimization framework for efficient and transferable coarse-graining of a model polymer","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"Adrian Menzel, Andrew W Phillips, Elnaz Hajizadeh, Hansani Weeratunge, Kate Smith-Miles, Ronald G Larson, Zakiya Shireen","submitted_at":"2022-04-28T05:43:50Z","abstract_excerpt":"This work presents a novel framework governing the development of an efficient, accurate, and transferable coarse-grained (CG) model of a polyether material. The proposed framework combines the two fundamentally different classical optimization approaches for the development of coarse-grained model parameters; namely bottom-up and top-down approaches. This is achieved through integrating the optimization algorithms into a machine learning (ML) model, trained using molecular dynamics (MD) simulation data. In the bottom-up approach, bonded interactions of the CG model are optimized using deep ne"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.13295","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2204.13295/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":"2204.13295","created_at":"2026-07-05T04:18:33.336427+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.13295v1","created_at":"2026-07-05T04:18:33.336427+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.13295","created_at":"2026-07-05T04:18:33.336427+00:00"},{"alias_kind":"pith_short_12","alias_value":"PNPSR7CCA5KZ","created_at":"2026-07-05T04:18:33.336427+00:00"},{"alias_kind":"pith_short_16","alias_value":"PNPSR7CCA5KZNX2C","created_at":"2026-07-05T04:18:33.336427+00:00"},{"alias_kind":"pith_short_8","alias_value":"PNPSR7CC","created_at":"2026-07-05T04:18:33.336427+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/PNPSR7CCA5KZNX2CSJD5RZKGOV","json":"https://pith.science/pith/PNPSR7CCA5KZNX2CSJD5RZKGOV.json","graph_json":"https://pith.science/api/pith-number/PNPSR7CCA5KZNX2CSJD5RZKGOV/graph.json","events_json":"https://pith.science/api/pith-number/PNPSR7CCA5KZNX2CSJD5RZKGOV/events.json","paper":"https://pith.science/paper/PNPSR7CC"},"agent_actions":{"view_html":"https://pith.science/pith/PNPSR7CCA5KZNX2CSJD5RZKGOV","download_json":"https://pith.science/pith/PNPSR7CCA5KZNX2CSJD5RZKGOV.json","view_paper":"https://pith.science/paper/PNPSR7CC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.13295&json=true","fetch_graph":"https://pith.science/api/pith-number/PNPSR7CCA5KZNX2CSJD5RZKGOV/graph.json","fetch_events":"https://pith.science/api/pith-number/PNPSR7CCA5KZNX2CSJD5RZKGOV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PNPSR7CCA5KZNX2CSJD5RZKGOV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PNPSR7CCA5KZNX2CSJD5RZKGOV/action/storage_attestation","attest_author":"https://pith.science/pith/PNPSR7CCA5KZNX2CSJD5RZKGOV/action/author_attestation","sign_citation":"https://pith.science/pith/PNPSR7CCA5KZNX2CSJD5RZKGOV/action/citation_signature","submit_replication":"https://pith.science/pith/PNPSR7CCA5KZNX2CSJD5RZKGOV/action/replication_record"}},"created_at":"2026-07-05T04:18:33.336427+00:00","updated_at":"2026-07-05T04:18:33.336427+00:00"}