{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:72NZYSSI6D7S7MITA5TGLT3NHH","short_pith_number":"pith:72NZYSSI","schema_version":"1.0","canonical_sha256":"fe9b9c4a48f0ff2fb113076665cf6d39ca20f59e74c5e25c3a94dfc21210e775","source":{"kind":"arxiv","id":"2602.16908","version":2},"attestation_state":"computed","paper":{"title":"Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","quant-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"A. Melnikov, A. Sagingalieva, A. Seth, D. Morozov, D. Tarpanov, G. Laskaris, G. Sai Gautam, J. Procelewska, R. Brasher","submitted_at":"2026-02-18T21:48:35Z","abstract_excerpt":"Allegro is a machine learning interatomic potential model designed to predict atomic properties in molecules using E(3) equivariant neural networks. When training this model, there tends to be a trade-off between accuracy and inference time. For this reason, we apply multi-objective hyperparameter optimization to both objectives. Additionally, we experiment with modified architectures by constructing variants of Allegro: one extended with additional classical layers and one incorporating quantum-classical hybrid layers. We evaluate all models on QM9, rMD17-aspirin, rMD17-benzene, and a self-ge"},"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":"2602.16908","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-02-18T21:48:35Z","cross_cats_sorted":["cs.LG","quant-ph"],"title_canon_sha256":"4f0dd37176db62c187e3b25ba10c8ad7b4c5d86bf12b6ff689dd53252ce35f1e","abstract_canon_sha256":"90981678db0cedb077e80807652679b662447abd7228df17302f0ecda2d32022"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:03:59.348072Z","signature_b64":"sIMuhNpEmxckbADO8giA5K5mAhHQZM7VDqkkk3fVNmIrQvI/6ler2l1fAdD/UG3aKtdiTRMj3V78RU2dB8SlCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe9b9c4a48f0ff2fb113076665cf6d39ca20f59e74c5e25c3a94dfc21210e775","last_reissued_at":"2026-06-08T01:03:59.347220Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:03:59.347220Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","quant-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"A. Melnikov, A. Sagingalieva, A. Seth, D. Morozov, D. Tarpanov, G. Laskaris, G. Sai Gautam, J. Procelewska, R. Brasher","submitted_at":"2026-02-18T21:48:35Z","abstract_excerpt":"Allegro is a machine learning interatomic potential model designed to predict atomic properties in molecules using E(3) equivariant neural networks. When training this model, there tends to be a trade-off between accuracy and inference time. For this reason, we apply multi-objective hyperparameter optimization to both objectives. Additionally, we experiment with modified architectures by constructing variants of Allegro: one extended with additional classical layers and one incorporating quantum-classical hybrid layers. We evaluate all models on QM9, rMD17-aspirin, rMD17-benzene, and a self-ge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.16908","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/2602.16908/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":"2602.16908","created_at":"2026-06-08T01:03:59.347326+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.16908v2","created_at":"2026-06-08T01:03:59.347326+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.16908","created_at":"2026-06-08T01:03:59.347326+00:00"},{"alias_kind":"pith_short_12","alias_value":"72NZYSSI6D7S","created_at":"2026-06-08T01:03:59.347326+00:00"},{"alias_kind":"pith_short_16","alias_value":"72NZYSSI6D7S7MIT","created_at":"2026-06-08T01:03:59.347326+00:00"},{"alias_kind":"pith_short_8","alias_value":"72NZYSSI","created_at":"2026-06-08T01:03:59.347326+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/72NZYSSI6D7S7MITA5TGLT3NHH","json":"https://pith.science/pith/72NZYSSI6D7S7MITA5TGLT3NHH.json","graph_json":"https://pith.science/api/pith-number/72NZYSSI6D7S7MITA5TGLT3NHH/graph.json","events_json":"https://pith.science/api/pith-number/72NZYSSI6D7S7MITA5TGLT3NHH/events.json","paper":"https://pith.science/paper/72NZYSSI"},"agent_actions":{"view_html":"https://pith.science/pith/72NZYSSI6D7S7MITA5TGLT3NHH","download_json":"https://pith.science/pith/72NZYSSI6D7S7MITA5TGLT3NHH.json","view_paper":"https://pith.science/paper/72NZYSSI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.16908&json=true","fetch_graph":"https://pith.science/api/pith-number/72NZYSSI6D7S7MITA5TGLT3NHH/graph.json","fetch_events":"https://pith.science/api/pith-number/72NZYSSI6D7S7MITA5TGLT3NHH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/72NZYSSI6D7S7MITA5TGLT3NHH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/72NZYSSI6D7S7MITA5TGLT3NHH/action/storage_attestation","attest_author":"https://pith.science/pith/72NZYSSI6D7S7MITA5TGLT3NHH/action/author_attestation","sign_citation":"https://pith.science/pith/72NZYSSI6D7S7MITA5TGLT3NHH/action/citation_signature","submit_replication":"https://pith.science/pith/72NZYSSI6D7S7MITA5TGLT3NHH/action/replication_record"}},"created_at":"2026-06-08T01:03:59.347326+00:00","updated_at":"2026-06-08T01:03:59.347326+00:00"}