{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00105034,0.000915302,0.0005975811,0.001678136,0.0005979201,0.00153765,0.001191563,0.0009573054,0.00961623],"category_scores_gemma":[0.0009450165,0.0004674303,0.0007784913,0.0007364854,0.0004137474,0.001127308,0.001715403,0.001576383,0.007162087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006463983,"about_ca_system_score_gemma":0.001519965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004446874,"about_ca_topic_score_gemma":0.0006064634,"domain_scores_codex":[0.9992482,0.00006352201,0.00003236278,0.000147401,0.000424982,0.00008341103],"domain_scores_gemma":[0.999558,0.0000919544,0.00005112833,0.0001267304,0.0001095528,0.00006267792],"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":[0.0005290124,0.0003160691,0.001252546,0.0005817264,0.0000887383,0.0008274495,0.0001919878,0.008797237,0.6839017,0.03203666,0.03096519,0.2405118],"study_design_scores_gemma":[0.0001325534,0.0005290625,0.0012658,0.00007466351,0.00005835499,0.0007893503,0.00006620242,0.06684829,0.5969102,0.01769966,0.3155055,0.0001203873],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05562399,0.002745312,0.7954687,0.001601841,0.0007371725,0.001185777,0.006725774,0.09724119,0.03867017],"genre_scores_gemma":[0.1490113,0.001911942,0.8243234,0.000521663,0.0001466343,0.001014186,0.007354313,0.001839379,0.01387712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00961623,"threshold_uncertainty_score":0.03216946,"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."}}