{"id":"W2621912989","doi":"10.1063/1.4985084","title":"Communication: DFT treatment of strong correlation in 3d transition-metal diatomics","year":2017,"lang":"en","type":"article","venue":"The Journal of Chemical Physics","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Diatomic molecule; Density functional theory; Electronic correlation; Transition metal; Work (physics); Chemistry; Electron; Atomic physics; Computational chemistry; Physics; Quantum mechanics; Molecule; Catalysis","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":[0.0005719063,0.000634311,0.0009449881,0.0006653683,0.0008217116,0.0009026917,0.001510122,0.001549662,0.03979618],"category_scores_gemma":[0.001579187,0.0001809327,0.0003362225,0.0008312307,0.00050887,0.001248445,0.001122544,0.001634834,0.007361082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004422208,"about_ca_system_score_gemma":0.0007415281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002347401,"about_ca_topic_score_gemma":0.002531681,"domain_scores_codex":[0.9995766,0.00007212681,0.00001414624,0.00003471717,0.000247562,0.00005474511],"domain_scores_gemma":[0.9992306,0.0003211519,0.00002940964,0.0001134621,0.0001980392,0.0001071958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003735036,0.0003043984,0.001010759,0.001450251,0.0001437306,0.0008537602,0.0002199883,0.1800211,0.007166474,0.2077228,0.4687356,0.1319977],"study_design_scores_gemma":[0.0001992776,0.000113163,0.0007939991,0.0002138451,0.00003388778,0.0002144352,0.00005340827,0.824448,0.0114373,0.06038862,0.1020215,0.00008259777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1661261,0.01932144,0.4449252,0.03722915,0.05617651,0.0004801888,0.01844771,0.007677379,0.2496162],"genre_scores_gemma":[0.7111229,0.01193635,0.1042778,0.007603068,0.01157377,0.0008723908,0.01193502,0.003383277,0.1372954],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03979618,"threshold_uncertainty_score":0.1331314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01809320848394484,"score_gpt":0.2870644818036258,"score_spread":0.268971273319681,"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."}}