{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WJW73LB6EITAXKMM6XWR2U7HB4","short_pith_number":"pith:WJW73LB6","schema_version":"1.0","canonical_sha256":"b26dfdac3e22260ba98cf5ed1d53e70f1b4c46819a04a830caf68a7f3c6af989","source":{"kind":"arxiv","id":"2403.01980","version":3},"attestation_state":"computed","paper":{"title":"Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"Albert Musaelian, Anders Johansson, Andrea Cepellotti, Blake R. Duschatko, Boris Kozinsky, Jarad A. Mason, Jingxuan Ding, Julia H. Yang, Kyle Bystrom, Lixin Sun, Malia B. Wenny, Nicola Molinari, Simon Batzner, Zachary A. H. Goodwin","submitted_at":"2024-03-04T12:23:32Z","abstract_excerpt":"Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as ``designer solvents'' as they can be mixed to precisely tailor the physiochemical properties. As using machine learning interatomic potentials (MLIPs) to simulate ILs is still relatively unexplored, several questions need to be answered to see if MLIPs can be transformative for ILs. Since ILs are often not pure, but are either mixed together or contain additives, we first demonstrate that a MLIP can be trained to be compositionally transfe"},"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":"2403.01980","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2024-03-04T12:23:32Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"66c0c3c70dc4dd329fd0938dffcf5a0780c567e5b4618bcafb6d8aec4f8ef8c4","abstract_canon_sha256":"fbee21fb28f53ee0ae56503b50635074c53fe0e72a89e3b05c758d518c4d62cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:43:53.704405Z","signature_b64":"E2r+ZvVeGKMzKg0iEw2OMXTvelwhgMr4q98U3dGBVu5EjX3PVZYDaQbKR/Y0RU3zR0rywwnV0vLHx3LgHiP6AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b26dfdac3e22260ba98cf5ed1d53e70f1b4c46819a04a830caf68a7f3c6af989","last_reissued_at":"2026-07-05T08:43:53.703903Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:43:53.703903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"Albert Musaelian, Anders Johansson, Andrea Cepellotti, Blake R. Duschatko, Boris Kozinsky, Jarad A. Mason, Jingxuan Ding, Julia H. Yang, Kyle Bystrom, Lixin Sun, Malia B. Wenny, Nicola Molinari, Simon Batzner, Zachary A. H. Goodwin","submitted_at":"2024-03-04T12:23:32Z","abstract_excerpt":"Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as ``designer solvents'' as they can be mixed to precisely tailor the physiochemical properties. As using machine learning interatomic potentials (MLIPs) to simulate ILs is still relatively unexplored, several questions need to be answered to see if MLIPs can be transformative for ILs. Since ILs are often not pure, but are either mixed together or contain additives, we first demonstrate that a MLIP can be trained to be compositionally transfe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.01980","kind":"arxiv","version":3},"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/2403.01980/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":"2403.01980","created_at":"2026-07-05T08:43:53.703965+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.01980v3","created_at":"2026-07-05T08:43:53.703965+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.01980","created_at":"2026-07-05T08:43:53.703965+00:00"},{"alias_kind":"pith_short_12","alias_value":"WJW73LB6EITA","created_at":"2026-07-05T08:43:53.703965+00:00"},{"alias_kind":"pith_short_16","alias_value":"WJW73LB6EITAXKMM","created_at":"2026-07-05T08:43:53.703965+00:00"},{"alias_kind":"pith_short_8","alias_value":"WJW73LB6","created_at":"2026-07-05T08:43:53.703965+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/WJW73LB6EITAXKMM6XWR2U7HB4","json":"https://pith.science/pith/WJW73LB6EITAXKMM6XWR2U7HB4.json","graph_json":"https://pith.science/api/pith-number/WJW73LB6EITAXKMM6XWR2U7HB4/graph.json","events_json":"https://pith.science/api/pith-number/WJW73LB6EITAXKMM6XWR2U7HB4/events.json","paper":"https://pith.science/paper/WJW73LB6"},"agent_actions":{"view_html":"https://pith.science/pith/WJW73LB6EITAXKMM6XWR2U7HB4","download_json":"https://pith.science/pith/WJW73LB6EITAXKMM6XWR2U7HB4.json","view_paper":"https://pith.science/paper/WJW73LB6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.01980&json=true","fetch_graph":"https://pith.science/api/pith-number/WJW73LB6EITAXKMM6XWR2U7HB4/graph.json","fetch_events":"https://pith.science/api/pith-number/WJW73LB6EITAXKMM6XWR2U7HB4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WJW73LB6EITAXKMM6XWR2U7HB4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WJW73LB6EITAXKMM6XWR2U7HB4/action/storage_attestation","attest_author":"https://pith.science/pith/WJW73LB6EITAXKMM6XWR2U7HB4/action/author_attestation","sign_citation":"https://pith.science/pith/WJW73LB6EITAXKMM6XWR2U7HB4/action/citation_signature","submit_replication":"https://pith.science/pith/WJW73LB6EITAXKMM6XWR2U7HB4/action/replication_record"}},"created_at":"2026-07-05T08:43:53.703965+00:00","updated_at":"2026-07-05T08:43:53.703965+00:00"}