{"id":"W4318486595","doi":"10.1038/s44160-022-00231-0","title":"The rise of self-driving labs in chemical and materials sciences","year":2023,"lang":"en","type":"article","venue":"Nature Synthesis","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":506,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Division of Chemical, Bioengineering, Environmental, and Transport Systems; Camille and Henry Dreyfus Foundation; National Science Foundation","keywords":"Pace; Computer science; Modular design; Automation; Status quo; Robotics; Implementation; Field (mathematics); Data science; Artificial intelligence; Sustainability; Engineering management; Systems engineering; Robot; Software engineering; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004749193,0.0001317756,0.0002460437,0.0001365322,0.0002540514,0.0001999996,0.0006821284,0.0001559458,0.0001474902],"category_scores_gemma":[0.003431194,0.00008448964,0.0000242878,0.0006293729,0.0005374611,0.0001584856,0.000308171,0.0001615714,0.00006972269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002295848,"about_ca_system_score_gemma":0.00005241631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004799771,"about_ca_topic_score_gemma":0.00001284956,"domain_scores_codex":[0.9981385,0.0003266001,0.0003229474,0.0003796055,0.0004462104,0.0003860844],"domain_scores_gemma":[0.9978788,0.001619137,0.0001614956,0.0002564156,0.00003839115,0.00004568902],"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":[0.000009862176,0.000009857016,0.002137978,0.00003689677,0.00000162422,0.00000354043,0.0001711695,0.00003024305,0.9953355,0.001518208,0.0001585373,0.0005865783],"study_design_scores_gemma":[0.0000593927,0.00001401215,0.01594333,0.00008517387,0.000006364238,0.000006586073,0.0001007932,0.0007659161,0.9811779,0.000983048,0.0007400204,0.0001174771],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975781,0.0001958151,0.000003105277,0.0009647185,0.0004816645,0.0001228371,0.00001247143,0.0001414563,0.0004998554],"genre_scores_gemma":[0.9956899,0.00008577896,0.004058698,0.00002386734,0.00006662012,0.00002634363,2.878209e-7,0.00001092654,0.00003753623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01415762,"threshold_uncertainty_score":0.4107707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005897296889927839,"score_gpt":0.2682118815063124,"score_spread":0.2623145846163845,"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."}}