{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KRNI3U7CC3W477ABNNPF327SPW","short_pith_number":"pith:KRNI3U7C","schema_version":"1.0","canonical_sha256":"545a8dd3e216edcffc016b5e5debf27db588410e1e527ba6d8bbea8f98b7ec72","source":{"kind":"arxiv","id":"2412.21072","version":1},"attestation_state":"computed","paper":{"title":"Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","cs.LG"],"primary_cat":"cond-mat.str-el","authors_text":"Chen Cheng, Gia-Wei Chern, Yang Yang, Yunhao Fan","submitted_at":"2024-12-30T16:44:11Z","abstract_excerpt":"The phase ordering kinetics of emergent orders in correlated electron systems is a fundamental topic in non-equilibrium physics, yet it remains largely unexplored. The intricate interplay between quasiparticles and emergent order-parameter fields could lead to unusual coarsening dynamics that is beyond the standard theories. However, accurate treatment of both quasiparticles and collective degrees of freedom is a multi-scale challenge in dynamical simulations of correlated electrons. Here we leverage modern machine learning (ML) methods to achieve a linear-scaling algorithm for simulating the "},"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":"2412.21072","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.str-el","submitted_at":"2024-12-30T16:44:11Z","cross_cats_sorted":["cond-mat.stat-mech","cs.LG"],"title_canon_sha256":"17299fe1c36b1c6d4ea03fda669a420af30920d39ec58a8561545d27b2f1b588","abstract_canon_sha256":"30452d29562e6f801f158eacdbf6093826f2bc742716bb11490836c6302a5a25"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:26.830744Z","signature_b64":"BYZEFZWCHIoYPmNVfAKW/q1Ptr9tQJ7GU7Sql5KTdvpdX9jBlBTmBdAENLNfftWA4uiB2nvy9gTtkyea3betAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"545a8dd3e216edcffc016b5e5debf27db588410e1e527ba6d8bbea8f98b7ec72","last_reissued_at":"2026-07-05T09:55:26.830246Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:26.830246Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","cs.LG"],"primary_cat":"cond-mat.str-el","authors_text":"Chen Cheng, Gia-Wei Chern, Yang Yang, Yunhao Fan","submitted_at":"2024-12-30T16:44:11Z","abstract_excerpt":"The phase ordering kinetics of emergent orders in correlated electron systems is a fundamental topic in non-equilibrium physics, yet it remains largely unexplored. The intricate interplay between quasiparticles and emergent order-parameter fields could lead to unusual coarsening dynamics that is beyond the standard theories. However, accurate treatment of both quasiparticles and collective degrees of freedom is a multi-scale challenge in dynamical simulations of correlated electrons. Here we leverage modern machine learning (ML) methods to achieve a linear-scaling algorithm for simulating the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.21072","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/2412.21072/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":"2412.21072","created_at":"2026-07-05T09:55:26.830304+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.21072v1","created_at":"2026-07-05T09:55:26.830304+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.21072","created_at":"2026-07-05T09:55:26.830304+00:00"},{"alias_kind":"pith_short_12","alias_value":"KRNI3U7CC3W4","created_at":"2026-07-05T09:55:26.830304+00:00"},{"alias_kind":"pith_short_16","alias_value":"KRNI3U7CC3W477AB","created_at":"2026-07-05T09:55:26.830304+00:00"},{"alias_kind":"pith_short_8","alias_value":"KRNI3U7C","created_at":"2026-07-05T09:55:26.830304+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24800","citing_title":"The Interplay of Thermal Melting and Pump Driven Melting of Charge Order: A Two-Temperature Study of the Holstein Model","ref_index":50,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KRNI3U7CC3W477ABNNPF327SPW","json":"https://pith.science/pith/KRNI3U7CC3W477ABNNPF327SPW.json","graph_json":"https://pith.science/api/pith-number/KRNI3U7CC3W477ABNNPF327SPW/graph.json","events_json":"https://pith.science/api/pith-number/KRNI3U7CC3W477ABNNPF327SPW/events.json","paper":"https://pith.science/paper/KRNI3U7C"},"agent_actions":{"view_html":"https://pith.science/pith/KRNI3U7CC3W477ABNNPF327SPW","download_json":"https://pith.science/pith/KRNI3U7CC3W477ABNNPF327SPW.json","view_paper":"https://pith.science/paper/KRNI3U7C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.21072&json=true","fetch_graph":"https://pith.science/api/pith-number/KRNI3U7CC3W477ABNNPF327SPW/graph.json","fetch_events":"https://pith.science/api/pith-number/KRNI3U7CC3W477ABNNPF327SPW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KRNI3U7CC3W477ABNNPF327SPW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KRNI3U7CC3W477ABNNPF327SPW/action/storage_attestation","attest_author":"https://pith.science/pith/KRNI3U7CC3W477ABNNPF327SPW/action/author_attestation","sign_citation":"https://pith.science/pith/KRNI3U7CC3W477ABNNPF327SPW/action/citation_signature","submit_replication":"https://pith.science/pith/KRNI3U7CC3W477ABNNPF327SPW/action/replication_record"}},"created_at":"2026-07-05T09:55:26.830304+00:00","updated_at":"2026-07-05T09:55:26.830304+00:00"}