{"id":"W4408014918","doi":"10.1021/acs.nanolett.4c04889","title":"Experimental Online Quantum Dots Charge Autotuning Using Neural Networks","year":2025,"lang":"en","type":"article","venue":"Nano Letters","topic":"Semiconductor Quantum Structures and Devices","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo; Institut quantique; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Institut Périmètre de physique théorique; Ontario Ministry of Economic Development, Job Creation and Trade; Innovation, Science and Economic Development Canada","keywords":"Quantum dot; Charge (physics); Artificial neural network; Nanotechnology; Materials science; Quantum; Optoelectronics; Computer science; Physics; Artificial intelligence; Quantum mechanics","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":[],"consensus_categories":[],"category_scores_codex":[0.00003984328,0.0001950066,0.0002022778,0.00007966275,0.0001862621,0.00008057347,0.0001804641,0.00004028949,0.0003215722],"category_scores_gemma":[0.000001326247,0.0001791857,0.0001169485,0.0001775198,0.00004653259,0.0001455751,0.00007628987,0.0001546824,0.000003600682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003529897,"about_ca_system_score_gemma":0.0000183459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003231154,"about_ca_topic_score_gemma":6.105376e-7,"domain_scores_codex":[0.9990325,0.00003217985,0.0002140686,0.0002739079,0.0001055172,0.0003418501],"domain_scores_gemma":[0.9996112,0.00002929639,0.0000872053,0.0002036587,0.00001519063,0.00005347065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001949774,0.00006268059,0.01869953,0.000008588733,0.00009008137,0.000005206522,0.0001902575,0.003678614,0.9653707,0.008175948,0.002378534,0.001320425],"study_design_scores_gemma":[0.002547924,0.00006397998,0.005719949,0.0002025201,0.0001330778,0.000007710004,0.001722069,0.8417611,0.1370436,0.0004274205,0.009214928,0.001155687],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959248,0.0004103514,0.001551237,0.0004322841,0.001263065,0.0001075178,0.00001655626,0.00005187779,0.0002423468],"genre_scores_gemma":[0.9963092,4.451373e-7,0.0002186058,0.002731415,0.0006092425,0.000004582673,0.0000590342,0.00001813761,0.00004929445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8380825,"threshold_uncertainty_score":0.7306982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01783500623881323,"score_gpt":0.2857771008662194,"score_spread":0.2679420946274062,"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."}}