{"id":"W4389489590","doi":"10.1103/physrevb.108.245122","title":"Dynamical variational Monte Carlo as a quantum impurity solver: Application to cluster dynamical mean field theory","year":2023,"lang":"en","type":"article","venue":"Physical review. B./Physical review. B","topic":"Physics of Superconductivity and Magnetism","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; Université de Sherbrooke","funders":"Canada First Research Excellence Fund","keywords":"Quantum Monte Carlo; Statistical physics; Solver; Monte Carlo method; Hubbard model; Physics; Cluster (spacecraft); Monte Carlo molecular modeling; Limit (mathematics); Monte Carlo method in statistical physics; Dynamic Monte Carlo method; Benchmark (surveying); Scaling; Quantum; Quantum mechanics; Hybrid Monte Carlo; Computer science; Mathematical optimization; Mathematics; Mathematical analysis; Markov chain Monte Carlo","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":[0.001238393,0.0003527837,0.0006804344,0.0004478348,0.0006641326,0.0007568341,0.001175403,0.0008981811,0.001271086],"category_scores_gemma":[0.002426264,0.0002856467,0.0004273173,0.00050799,0.001006318,0.0005386907,0.0008500767,0.0009021453,0.0002057354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001007223,"about_ca_system_score_gemma":0.001662007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009611249,"about_ca_topic_score_gemma":0.008420655,"domain_scores_codex":[0.9996578,0.0001862676,0.000009198809,0.00002693427,0.00009031282,0.00002957895],"domain_scores_gemma":[0.9989585,0.0006649299,0.00005513968,0.00008387007,0.0001629248,0.00007462981],"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":[0.0000372721,0.00004713226,0.0006103134,0.00006828941,0.00004283165,0.00006353865,0.00008530349,0.8100797,0.002279069,0.1730217,0.001072557,0.01259221],"study_design_scores_gemma":[0.000003744966,0.000002415249,0.0000190145,0.000001643062,8.809629e-7,0.000002476901,0.000002107106,0.9935456,0.0001346719,0.006031729,0.0002538237,0.00000189824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05426522,0.0004654412,0.9356853,0.0006709737,0.000104709,0.000093601,0.000108237,0.000321162,0.008285341],"genre_scores_gemma":[0.5502438,0.0003588888,0.4446801,0.0002162234,0.00007839993,0.0002529823,0.0001495582,0.0002984005,0.003721703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009611249,"threshold_uncertainty_score":0.01911062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0106907588854915,"score_gpt":0.3378542013813917,"score_spread":0.3271634424959002,"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."}}