{"id":"W4415431826","doi":"10.1021/acs.jctc.5c01333","title":"Capturing Electron Correlation with Machine Learning through a Data-Driven CASPT2 Framework","year":2025,"lang":"en","type":"article","venue":"Journal of Chemical Theory and Computation","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dairy Farmers of Ontario; University of Toronto","funders":"Division of Chemistry","keywords":"Electronic correlation; Correlation; Perturbation (astronomy); Complete active space; Perturbation theory (quantum mechanics); Set (abstract data type); Space (punctuation); Parameter space","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":[0.002183857,0.0006809209,0.0009459971,0.0008064443,0.0006564413,0.001031328,0.002778358,0.001126446,0.001271891],"category_scores_gemma":[0.005220982,0.000302805,0.0006585807,0.0008196563,0.000952941,0.00198799,0.001228943,0.001834008,0.0002622523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006701522,"about_ca_system_score_gemma":0.001608708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001470305,"about_ca_topic_score_gemma":0.001581996,"domain_scores_codex":[0.9992375,0.0003355721,0.00002707569,0.00007931932,0.0002789424,0.00004157123],"domain_scores_gemma":[0.9974401,0.001423459,0.0001733134,0.0004980356,0.0003733655,0.00009172456],"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":[0.00006941495,0.0001540555,0.001058296,0.000118134,0.00008593448,0.0001055669,0.0000635319,0.8526079,0.003405749,0.09302513,0.001859072,0.04744719],"study_design_scores_gemma":[0.000004460595,0.000008441642,0.00003535366,0.000001567259,0.000001678047,0.000004982403,0.00000256535,0.9873683,0.0004905383,0.01191224,0.000166319,0.000003516169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03634813,0.0001204231,0.959944,0.0002974024,0.00003396573,0.00008216388,0.0001893535,0.0007397507,0.002244734],"genre_scores_gemma":[0.5528553,0.0001465318,0.4448059,0.0001977613,0.00006012115,0.0003264878,0.0004671173,0.0001589226,0.0009819322],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002778358,"threshold_uncertainty_score":0.01154947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151249226718793,"score_gpt":0.2942273946020841,"score_spread":0.2827149023348962,"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."}}