{"id":"W4389680398","doi":"10.26434/chemrxiv-2023-t29k7","title":"Catalyzing Change: The Power of Computational Asymmetric Catalysis","year":2023,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Enantioselective synthesis; Diastereomer; Mechanism (biology); Computer science; Catalysis; Stereoselectivity; Expressive power; Field (mathematics); Biochemical engineering; Chemistry; Organocatalysis; Combinatorial chemistry; Nanotechnology; Artificial intelligence; Theoretical computer science; Mathematics; Organic chemistry; Materials science; Engineering; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002286791,0.0002934484,0.0004902991,0.0003081834,0.0001972523,0.0002101772,0.001699773,0.0001825702,0.0004682749],"category_scores_gemma":[0.0009145473,0.0002159708,0.0001766371,0.0008485604,0.000388789,0.0001162777,0.001946627,0.0003939164,0.0007924201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007364875,"about_ca_system_score_gemma":0.0001465808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008394602,"about_ca_topic_score_gemma":0.00001288948,"domain_scores_codex":[0.9973174,0.0001790253,0.0005785996,0.0007263027,0.0008175282,0.0003810894],"domain_scores_gemma":[0.9974968,0.0004898487,0.0007053984,0.001002936,0.0002282727,0.00007674363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001090487,0.0005843251,0.02153689,0.003562957,0.0003425704,0.00009721023,0.02350748,0.413039,0.5128325,0.003813107,0.0162362,0.004338684],"study_design_scores_gemma":[0.0009397317,0.0001650958,0.2202831,0.001167813,0.000505565,0.00005199707,0.0008292185,0.05705679,0.6802819,0.03319067,0.00279364,0.002734445],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910836,0.0003829144,0.002551835,0.001580295,0.002956388,0.0004722178,0.00004980556,0.0002796801,0.0006433275],"genre_scores_gemma":[0.994632,0.00001833256,0.004396641,0.0001109698,0.0002539787,0.0001264008,0.0001270682,0.00004662809,0.0002879696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3559822,"threshold_uncertainty_score":0.9999856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04525156477955491,"score_gpt":0.3034367683233409,"score_spread":0.258185203543786,"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."}}