{"id":"W4320483496","doi":"10.1039/d2ya00316c","title":"Machine learning assisted binary alloy catalyst design for the electroreduction of CO<sub>2</sub> to C<sub>2</sub> products","year":2023,"lang":"en","type":"article","venue":"Energy Advances","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; National Research Council Canada; Compute Canada","keywords":"Alloy; Catalysis; Materials science; Binary number; Binary alloy; Chemical engineering; Metallurgy; Chemistry; Engineering; Organic chemistry; Mathematics; Arithmetic","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":[0.0002245694,0.0003183347,0.0004512185,0.0002604915,0.0001837311,0.000442629,0.0005161043,0.0004257709,0.001374629],"category_scores_gemma":[0.0004339578,0.000202066,0.0002436918,0.0002571949,0.0001470068,0.0003150564,0.0001984673,0.0003973572,0.0004456619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005074878,"about_ca_system_score_gemma":0.0005156703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001122543,"about_ca_topic_score_gemma":0.003195285,"domain_scores_codex":[0.9998909,0.00001918075,0.00000507922,0.00001624225,0.00005367451,0.00001492634],"domain_scores_gemma":[0.9999336,0.00001914792,0.000009182965,0.000006344061,0.00002672032,0.00000503704],"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":[0.0004609633,0.0002820978,0.001645701,0.0007677063,0.0001023565,0.0001690305,0.00007843141,0.5250929,0.2531024,0.02728425,0.003541549,0.1874726],"study_design_scores_gemma":[0.00001904223,0.0001360385,0.0002504105,0.000007145474,0.00001464423,0.000032511,0.00001305204,0.9450824,0.04862723,0.001868223,0.003939372,0.000009912923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3233482,0.002987013,0.6481676,0.0007099449,0.0001832299,0.0002181349,0.0003890522,0.00141394,0.02258286],"genre_scores_gemma":[0.8669363,0.0006780164,0.1282593,0.00008167304,0.00001365248,0.0001090799,0.0002063724,0.00007019859,0.003645335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001374629,"threshold_uncertainty_score":0.004598618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01833485623878388,"score_gpt":0.2599952278071171,"score_spread":0.2416603715683332,"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."}}