{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:AHE4LVRYDX6SJOWSMNREIQZRIW","short_pith_number":"pith:AHE4LVRY","schema_version":"1.0","canonical_sha256":"01c9c5d6381dfd24bad2636244433145b09d855e3d99a850eba946b1e279e372","source":{"kind":"arxiv","id":"2211.16684","version":1},"attestation_state":"computed","paper":{"title":"Capturing long-range interaction with reciprocal space neural network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","physics.chem-ph","physics.comp-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Hongjun Xiang, Hongyu Yu, Liangliang Hong, Shiyou Chen, Xingao Gong","submitted_at":"2022-11-30T02:10:48Z","abstract_excerpt":"Machine Learning (ML) interatomic models and potentials have been widely employed in simulations of materials. Long-range interactions often dominate in some ionic systems whose dynamics behavior is significantly influenced. However, the long-range effect such as Coulomb and Van der Wales potential is not considered in most ML interatomic potentials. To address this issue, we put forward a method that can take long-range effects into account for most ML local interatomic models with the reciprocal space neural network. The structure information in real space is firstly transformed into recipro"},"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":"2211.16684","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2022-11-30T02:10:48Z","cross_cats_sorted":["cs.LG","physics.chem-ph","physics.comp-ph"],"title_canon_sha256":"48e70064eeed677b6554de03be674f95fc964dd633e5f85838635b5e5fd9be70","abstract_canon_sha256":"af35c79e84624612f754731abbdb4e46fef175bc707c92e54e3a016ac8f8a431"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:21:09.784736Z","signature_b64":"0yDqe2nuwNXOYylJnIHFuRuhHJaCK1Qf8vfE910ERcWj9FBeGf48Oho0Anm+3mlCAhj1JjxW+NCIB93gPno6AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01c9c5d6381dfd24bad2636244433145b09d855e3d99a850eba946b1e279e372","last_reissued_at":"2026-07-05T05:21:09.784237Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:21:09.784237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Capturing long-range interaction with reciprocal space neural network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","physics.chem-ph","physics.comp-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Hongjun Xiang, Hongyu Yu, Liangliang Hong, Shiyou Chen, Xingao Gong","submitted_at":"2022-11-30T02:10:48Z","abstract_excerpt":"Machine Learning (ML) interatomic models and potentials have been widely employed in simulations of materials. Long-range interactions often dominate in some ionic systems whose dynamics behavior is significantly influenced. However, the long-range effect such as Coulomb and Van der Wales potential is not considered in most ML interatomic potentials. To address this issue, we put forward a method that can take long-range effects into account for most ML local interatomic models with the reciprocal space neural network. The structure information in real space is firstly transformed into recipro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.16684","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/2211.16684/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":"2211.16684","created_at":"2026-07-05T05:21:09.784297+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.16684v1","created_at":"2026-07-05T05:21:09.784297+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.16684","created_at":"2026-07-05T05:21:09.784297+00:00"},{"alias_kind":"pith_short_12","alias_value":"AHE4LVRYDX6S","created_at":"2026-07-05T05:21:09.784297+00:00"},{"alias_kind":"pith_short_16","alias_value":"AHE4LVRYDX6SJOWS","created_at":"2026-07-05T05:21:09.784297+00:00"},{"alias_kind":"pith_short_8","alias_value":"AHE4LVRY","created_at":"2026-07-05T05:21:09.784297+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.14302","citing_title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AHE4LVRYDX6SJOWSMNREIQZRIW","json":"https://pith.science/pith/AHE4LVRYDX6SJOWSMNREIQZRIW.json","graph_json":"https://pith.science/api/pith-number/AHE4LVRYDX6SJOWSMNREIQZRIW/graph.json","events_json":"https://pith.science/api/pith-number/AHE4LVRYDX6SJOWSMNREIQZRIW/events.json","paper":"https://pith.science/paper/AHE4LVRY"},"agent_actions":{"view_html":"https://pith.science/pith/AHE4LVRYDX6SJOWSMNREIQZRIW","download_json":"https://pith.science/pith/AHE4LVRYDX6SJOWSMNREIQZRIW.json","view_paper":"https://pith.science/paper/AHE4LVRY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.16684&json=true","fetch_graph":"https://pith.science/api/pith-number/AHE4LVRYDX6SJOWSMNREIQZRIW/graph.json","fetch_events":"https://pith.science/api/pith-number/AHE4LVRYDX6SJOWSMNREIQZRIW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AHE4LVRYDX6SJOWSMNREIQZRIW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AHE4LVRYDX6SJOWSMNREIQZRIW/action/storage_attestation","attest_author":"https://pith.science/pith/AHE4LVRYDX6SJOWSMNREIQZRIW/action/author_attestation","sign_citation":"https://pith.science/pith/AHE4LVRYDX6SJOWSMNREIQZRIW/action/citation_signature","submit_replication":"https://pith.science/pith/AHE4LVRYDX6SJOWSMNREIQZRIW/action/replication_record"}},"created_at":"2026-07-05T05:21:09.784297+00:00","updated_at":"2026-07-05T05:21:09.784297+00:00"}