{"id":"W4396659606","doi":"10.1002/jcc.27384","title":"Range‐separated density functional theory using multiresolution analysis and quantum computing","year":2024,"lang":"en","type":"article","venue":"Journal of Computational Chemistry","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; Canadian Institute for Advanced Research; McGill University; GLS Industries (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; University of Toronto; Google; U.S. Department of Energy","keywords":"Density functional theory; Atomic orbital; Basis set; Wave function; Qubit; Statistical physics; Quantum; Range (aeronautics); Partition (number theory); Quantum mechanics; Quantum chemistry; Basis (linear algebra); Computer science; Mathematics; Physics; Molecule; Materials science; Combinatorics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009657775,0.0005805434,0.0009554464,0.000662134,0.0004637115,0.0008977759,0.00118403,0.0008680981,0.001764682],"category_scores_gemma":[0.001621687,0.0003371675,0.0009382756,0.000984747,0.0006929497,0.0009846283,0.0009174813,0.001377385,0.0003888799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006271901,"about_ca_system_score_gemma":0.000876963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002318414,"about_ca_topic_score_gemma":0.001883355,"domain_scores_codex":[0.99945,0.0002772931,0.00001786427,0.00002910596,0.0001950318,0.0000307041],"domain_scores_gemma":[0.9994335,0.0003059208,0.00004165479,0.0001187103,0.00007857879,0.00002152027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006200858,0.0001082401,0.0005509867,0.000376621,0.0001398066,0.0001766327,0.0001104285,0.6756406,0.01083525,0.248314,0.001317633,0.06236779],"study_design_scores_gemma":[0.000006494199,0.00001480109,0.00007039359,0.000009071804,0.000004426087,0.00001211854,0.000007040074,0.9864916,0.0007815535,0.01191317,0.0006820225,0.000007221797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03390392,0.001725358,0.9545088,0.0003877328,0.0000879858,0.0001119407,0.0001043021,0.0002992985,0.008870658],"genre_scores_gemma":[0.4452142,0.001183299,0.5509524,0.0001243025,0.00005722041,0.0003348596,0.00017331,0.0001493825,0.001811079],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002318414,"threshold_uncertainty_score":0.005903482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01263378597446456,"score_gpt":0.2606558989150064,"score_spread":0.2480221129405418,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}