{"id":"W3042241440","doi":"10.1103/physrevlett.126.032001","title":"Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models","year":2021,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum many-body systems","field":"Physics and Astronomy","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute","funders":"Ontario Ministry of Economic Development, Job Creation and Trade; Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; Government of Canada; Ministero dello Sviluppo Economico; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada","keywords":"Markov chain Monte Carlo; Statistical physics; Computer science; Observable; Generative grammar; Lattice (music); Monte Carlo method; Markov chain; Field (mathematics); Applied mathematics; Artificial intelligence; Machine learning; Physics; Mathematics; Statistics; Quantum mechanics; Bayesian probability","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":[0.001876561,0.0005168789,0.0009849267,0.0009228648,0.0006291269,0.001336411,0.001730492,0.001199078,0.001762013],"category_scores_gemma":[0.007509601,0.0008532233,0.0006476833,0.0006000052,0.002276306,0.002616496,0.001802992,0.002196077,0.0002339078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001558482,"about_ca_system_score_gemma":0.001218654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006434846,"about_ca_topic_score_gemma":0.006232071,"domain_scores_codex":[0.9996027,0.0001992493,0.00001242145,0.00004869741,0.00008836103,0.00004853209],"domain_scores_gemma":[0.9972842,0.001866448,0.0002410875,0.0002349988,0.0001448126,0.00022853],"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":[0.00006321111,0.00005402403,0.001451864,0.00004031641,0.00003173078,0.00007040634,0.00006229155,0.8803137,0.001403041,0.1106529,0.0005356835,0.005320753],"study_design_scores_gemma":[0.000003947039,0.000002164035,0.00006792672,0.000002054921,8.53009e-7,0.000002755396,0.000003332008,0.9750119,0.0001179926,0.02473464,0.00004821032,0.000004199226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3228334,0.0004762194,0.6696601,0.001353704,0.00007542844,0.00005254667,0.0002961443,0.000858481,0.004393945],"genre_scores_gemma":[0.9479049,0.0001619536,0.05010001,0.0001635527,0.00003941447,0.00005068555,0.0002657314,0.0001184427,0.001195348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006434846,"threshold_uncertainty_score":0.01279479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01467868579866028,"score_gpt":0.2714636709559238,"score_spread":0.2567849851572635,"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."}}