{"id":"W4403687003","doi":"10.1039/d4dd00228h","title":"Combining Hammett <i>σ</i> constants for Δ-machine learning and catalyst discovery","year":2024,"lang":"en","type":"article","venue":"Digital Discovery","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"Canada First Research Excellence Fund; University of Toronto; Natural Sciences and Engineering Research Council of Canada; European Commission; Horizon 2020 Framework Programme; Canadian Institute for Advanced Research","keywords":"Catalysis; Hammett equation; Chemistry; Psychology; Physics; Reaction rate constant; Organic chemistry; Quantum mechanics; Kinetics","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":[0.003536209,0.001432816,0.001760119,0.001302102,0.0006440249,0.001810979,0.003077321,0.001536934,0.003543029],"category_scores_gemma":[0.008196712,0.0007825539,0.001775229,0.001587572,0.001091415,0.003559729,0.001369784,0.003175245,0.00169148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00148014,"about_ca_system_score_gemma":0.002013061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003215605,"about_ca_topic_score_gemma":0.004894899,"domain_scores_codex":[0.99867,0.0006442592,0.00007540368,0.0002256142,0.0002753965,0.0001094707],"domain_scores_gemma":[0.996309,0.002365982,0.000242905,0.0006115646,0.0003407013,0.0001298552],"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":[0.0003676517,0.0004820001,0.001855528,0.0006521498,0.0003056868,0.0001693156,0.00008224373,0.5618531,0.01249397,0.284144,0.004729747,0.1328645],"study_design_scores_gemma":[0.00001539439,0.00007612036,0.00007029408,0.00001082139,0.00002656209,0.00003060636,0.000004962776,0.9113266,0.003072489,0.08410197,0.001234163,0.00003003585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01923233,0.001332373,0.9731817,0.0009917216,0.0001382598,0.00008625232,0.000309142,0.0006347784,0.004093494],"genre_scores_gemma":[0.5741469,0.00344464,0.4043497,0.00111048,0.0003379195,0.0006799185,0.001092859,0.0003414821,0.01449608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003543029,"threshold_uncertainty_score":0.01870149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01045772173791185,"score_gpt":0.2600238427901925,"score_spread":0.2495661210522807,"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."}}