{"id":"W2888436131","doi":"10.1021/acscentsci.8b00307","title":"Phoenics: A Bayesian Optimizer for Chemistry","year":2018,"lang":"en","type":"article","venue":"ACS Central Science","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":384,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; Canadian Institute for Advanced Research","funders":"Division of Chemistry; Harvard University","keywords":"Computer science; Computation; Probabilistic logic; Bayesian probability; Bayesian optimization; Set (abstract data type); Kernel (algebra); Sampling (signal processing); Mathematical optimization; Machine learning; Artificial intelligence; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002973815,0.002024241,0.001864125,0.001158966,0.0005938378,0.001837534,0.002172794,0.001996539,0.01212228],"category_scores_gemma":[0.008845495,0.001268093,0.001704318,0.0009593145,0.001184675,0.001649862,0.002393968,0.00286101,0.004059738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001351977,"about_ca_system_score_gemma":0.003095965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003476307,"about_ca_topic_score_gemma":0.004636269,"domain_scores_codex":[0.9988315,0.0004070184,0.0000579877,0.0002149169,0.0003988119,0.00008988894],"domain_scores_gemma":[0.9979961,0.001198002,0.0001575827,0.0002375473,0.0003163354,0.00009440116],"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":[0.0002203623,0.0001259415,0.0008557236,0.0004896726,0.0001651011,0.00006542393,0.00005878125,0.8143649,0.003238076,0.04434451,0.01472042,0.1213511],"study_design_scores_gemma":[0.00003731806,0.00003153439,0.00006479408,0.00002748412,0.00001163959,0.00001570877,0.000005550354,0.9789422,0.001484855,0.01357907,0.005787201,0.00001265687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.001549709,0.0002433477,0.9905749,0.0001481296,0.00004662252,0.00007903744,0.0003124459,0.003823977,0.003221788],"genre_scores_gemma":[0.07212763,0.0004783066,0.9171234,0.0004243587,0.00008039711,0.0008968453,0.001293373,0.003642789,0.003932873],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01212228,"threshold_uncertainty_score":0.04055303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00845738216555129,"score_gpt":0.270516171670086,"score_spread":0.2620587895045347,"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."}}