{"id":"W4244413936","doi":"10.26434/chemrxiv.13008500.v2","title":"Automatic discovery of chemical reactions using imposed activation","year":2020,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Canadian Institute for Advanced Research; University of Toronto","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Advanced Research Projects Agency; Defense Advanced Research Projects Agency","keywords":"Reactivity (psychology); Chemical reaction; Drug discovery; Chemistry; Natural product; Biochemical engineering; Decomposition; Computer science; Combinatorial chemistry; Computational chemistry; Molecule; Quantum chemical; Biological system; 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":[0.0006874432,0.001289268,0.001085408,0.001189389,0.0006325087,0.00124945,0.001714815,0.001368833,0.005733975],"category_scores_gemma":[0.002050986,0.0006613646,0.001508565,0.0008541405,0.0009598854,0.001561419,0.001345836,0.001523795,0.001453032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007354926,"about_ca_system_score_gemma":0.001593496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009421415,"about_ca_topic_score_gemma":0.001629685,"domain_scores_codex":[0.9995478,0.0001152219,0.00002446354,0.0001135261,0.0001601905,0.00003889012],"domain_scores_gemma":[0.9989582,0.0005676702,0.0001377281,0.0002036143,0.00008329446,0.00004947078],"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":[0.000452008,0.0001988356,0.002079074,0.0007357941,0.0001254553,0.0004406885,0.00008895839,0.7727042,0.04947453,0.07565331,0.004366828,0.09368025],"study_design_scores_gemma":[0.00002238379,0.00002754395,0.0001035909,0.000006898654,0.00001028181,0.00002600155,0.000006644977,0.9717894,0.008296401,0.01805948,0.001639722,0.00001178013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.13614,0.0006089674,0.8351791,0.0004594852,0.0001729264,0.0002652455,0.001531908,0.009021843,0.01662045],"genre_scores_gemma":[0.687623,0.0006105305,0.3035356,0.0001596068,0.00007779165,0.0005296483,0.002687746,0.0008737309,0.003902459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005733975,"threshold_uncertainty_score":0.01918203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03726095429326588,"score_gpt":0.3033747144374899,"score_spread":0.266113760144224,"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."}}