{"id":"W3156633173","doi":"10.1016/j.checat.2021.03.003","title":"Machine-learning-accelerated discovery of single-atom catalysts based on bidirectional activation mechanism","year":2021,"lang":"en","type":"article","venue":"Chem Catalysis","topic":"Ammonia Synthesis and Nitrogen Reduction","field":"Chemical Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Rational design; Catalysis; Mechanism (biology); Density functional theory; Selectivity; Atom (system on chip); Chemistry; Biological system; Reaction mechanism; Biochemical engineering; Work (physics); Nitrogen atom; Computer science; Computational chemistry; Combinatorial chemistry; Nanotechnology; Materials science; Physics; Thermodynamics; Biology; Organic chemistry; Engineering","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":[0.0005118503,0.0004344195,0.0005635325,0.0003602514,0.0002059106,0.0003714903,0.0007076079,0.0005599657,0.0007833914],"category_scores_gemma":[0.001045809,0.000197274,0.0004818809,0.0002687336,0.0004632876,0.0007257941,0.0004514093,0.0008852725,0.0002003601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000457049,"about_ca_system_score_gemma":0.0005581873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009129212,"about_ca_topic_score_gemma":0.001261439,"domain_scores_codex":[0.999851,0.00004190229,0.000007093476,0.00002189007,0.00005810958,0.00001990557],"domain_scores_gemma":[0.9997873,0.0001220105,0.00002038159,0.00003142123,0.0000281411,0.00001080536],"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":[0.000111891,0.0001499838,0.001847689,0.0005354311,0.00006299844,0.0001699513,0.00005573591,0.7741415,0.03320636,0.1547068,0.0008169645,0.03419482],"study_design_scores_gemma":[0.000006159351,0.00002339227,0.00007777878,0.000004502205,0.000003822685,0.000009904477,0.000003577841,0.9886724,0.003329447,0.007629055,0.0002363602,0.000003457393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.514398,0.002114339,0.4624672,0.0008485604,0.0001287392,0.0001261037,0.0003084391,0.0006047858,0.0190038],"genre_scores_gemma":[0.9661184,0.0007264561,0.03213917,0.00006009833,0.00001477941,0.00008786025,0.0001488074,0.00003229671,0.0006721892],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0009129212,"threshold_uncertainty_score":0.003316104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01761283031597533,"score_gpt":0.2155511961842239,"score_spread":0.1979383658682486,"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."}}