{"id":"W4308909327","doi":"10.1002/adma.202207070","title":"A Materials Acceleration Platform for Organic Laser Discovery","year":2022,"lang":"en","type":"article","venue":"Advanced Materials","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto","funders":"Defense Advanced Research Projects Agency; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Canada Foundation for Innovation","keywords":"Nanotechnology; Materials science; Characterization (materials science); Workflow; Automation; Lasing threshold; Commercialization; Laser; Identification (biology); Computer science; Systems engineering; Mechanical engineering; Optoelectronics; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002861335,0.0003862628,0.0006193717,0.0001406524,0.001129104,0.0008665617,0.0009445652,0.00007616987,0.0234754],"category_scores_gemma":[0.0005864167,0.0003679861,0.00006917729,0.0002487362,0.0001115434,0.001716157,0.0006865119,0.00009909343,0.0003039711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003233469,"about_ca_system_score_gemma":0.0001722425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007204097,"about_ca_topic_score_gemma":0.00001283785,"domain_scores_codex":[0.9963223,0.0003096321,0.0008828477,0.0008850035,0.0007945615,0.0008056498],"domain_scores_gemma":[0.9982235,0.0002276628,0.0006029033,0.0006929887,0.0001498169,0.0001031292],"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":[0.0005239947,0.00007109581,0.000008579597,0.00009060149,0.000006068953,0.000006106726,0.0001892306,0.002585665,0.9930085,0.002625371,0.0007305248,0.0001542516],"study_design_scores_gemma":[0.0010132,0.0003427959,0.0001705246,0.00001823236,0.00002244041,0.00003851522,0.0001501181,0.00003206624,0.9838079,0.004098181,0.009827705,0.0004783208],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846293,0.00002789259,0.005158828,0.0003151408,0.006545121,0.001327305,0.001446693,0.0003705262,0.0001792013],"genre_scores_gemma":[0.9820655,0.000009983381,0.013144,0.0006916563,0.0004523459,0.001572977,0.0003235124,0.0001011344,0.001638891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02317143,"threshold_uncertainty_score":0.9998772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01450629803404616,"score_gpt":0.2709881661592834,"score_spread":0.2564818681252372,"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."}}