{"paper":{"title":"Calculate electronic excited states using neural networks with effective core potential","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.atom-ph","authors_text":"Chenglong Qin, Gang Jiang, Jinde Liu, Xi He","submitted_at":"2023-12-23T10:32:52Z","abstract_excerpt":"The essence of atomic structure theory, quantum chemistry, and computational materials science is solving the multi-electron stationary Schr\\\"odinger equation. The Quantum Monte Carlo-based neural network wave function method has surpassed traditional post-Hartree-Fock methods in precision across various systems. However, its energy uncertainty is limited to 0.01%, posing challenges in accurately determining excited states and ionization energies, especially for elements beyond the fourth period. Using effective core potentials to account for inner electrons enhances the precision of vertical "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.15216","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/2312.15216/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"}