{"id":"W4322725964","doi":"10.1021/acs.jctc.2c01058","title":"Toward DMC Accuracy Across Chemical Space with Scalable Δ-QML","year":2023,"lang":"en","type":"article","venue":"Journal of Chemical Theory and Computation","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"H2020 European Research Council; National Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel Materials; European Commission; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Chemical space; Scalability; Quantum Monte Carlo; Scaling; Monte Carlo method; Statistical physics; Computer science; Quantum chemical; Dissociation (chemistry); Quantum; Diffusion Monte Carlo; Basis set; Quantum chemistry; Molecule; Bond-dissociation energy; Physics; Chemistry; Quantum mechanics; Mathematics; Physical chemistry; Hybrid Monte Carlo; Statistics; Drug discovery","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003263946,0.0008294999,0.001267264,0.0008655512,0.0009261408,0.001434843,0.003312816,0.001525936,0.002732682],"category_scores_gemma":[0.0104429,0.0007193445,0.0007550842,0.0009158637,0.001608066,0.002958721,0.00234086,0.003099831,0.0007075017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002089664,"about_ca_system_score_gemma":0.002712875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01123988,"about_ca_topic_score_gemma":0.009574395,"domain_scores_codex":[0.9990127,0.0003556134,0.00005274125,0.00009631398,0.000412422,0.00007015751],"domain_scores_gemma":[0.9959465,0.002287154,0.0001949856,0.0008574629,0.0005934891,0.0001204934],"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":[0.00006673938,0.00006435971,0.0009152076,0.0001734963,0.00002891799,0.00006379659,0.00009586479,0.9108363,0.002076825,0.07002375,0.00147075,0.01418407],"study_design_scores_gemma":[0.000004635506,0.000005560454,0.00002482352,0.00000765868,0.000001269837,0.000003212079,0.000007918284,0.9893266,0.0003820916,0.009836019,0.000397488,0.000002735932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07070547,0.00119708,0.9073232,0.002158718,0.0001932529,0.0001297818,0.0006795656,0.002324994,0.01528792],"genre_scores_gemma":[0.6067853,0.0007781834,0.3869926,0.00068306,0.00008645096,0.0003661649,0.000852519,0.0007543031,0.002701379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01123988,"threshold_uncertainty_score":0.02234894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01714000145651898,"score_gpt":0.3080300546022815,"score_spread":0.2908900531457625,"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."}}