{"id":"W3132811071","doi":"10.1109/ieeeconf35879.2020.9329711","title":"A Machine Learning-Based Approach to Synthesize Multilayer Metasurfaces","year":2020,"lang":"en","type":"article","venue":"","topic":"Metamaterials and Metasurfaces Applications","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Set (abstract data type); Process (computing); Coupling (piping); Degrees of freedom (physics and chemistry); Layer (electronics); Dual mode; Inverse; Scattering; Materials science; Electronic engineering; Optics; Nanotechnology; Engineering; Physics; Geometry; 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":[0.0003052968,0.0004358432,0.0003258338,0.0003320264,0.0001660706,0.0004978105,0.0005304509,0.0005893208,0.001166744],"category_scores_gemma":[0.000663054,0.0002302739,0.0004549901,0.0003091926,0.0003507484,0.0004800947,0.0005472393,0.0008676308,0.0004954398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004183422,"about_ca_system_score_gemma":0.0003193229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002815164,"about_ca_topic_score_gemma":0.0004730596,"domain_scores_codex":[0.9998389,0.00002761387,0.00001147306,0.00003775722,0.00006640966,0.00001785576],"domain_scores_gemma":[0.9998048,0.00007654269,0.00002853718,0.00003890116,0.00004142492,0.000009872762],"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":[0.00008619815,0.0001470061,0.0005961907,0.0002635861,0.00005731924,0.0001014286,0.0000800551,0.3826166,0.3221083,0.03504695,0.00129995,0.2575963],"study_design_scores_gemma":[0.000005018588,0.00004487914,0.00006710071,0.000006942109,0.000004640425,0.00003689588,0.000004461977,0.9633448,0.03042356,0.003866231,0.00218852,0.000006899288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008210679,0.00009242323,0.9892964,0.0001014704,0.0000242324,0.00001833618,0.000028355,0.0003302384,0.001897929],"genre_scores_gemma":[0.2550802,0.0001706758,0.741987,0.0001227895,0.00003031374,0.0001045015,0.0001064976,0.00008294285,0.002315061],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001166744,"threshold_uncertainty_score":0.003903091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05088899828200887,"score_gpt":0.2562700828773292,"score_spread":0.2053810845953203,"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."}}