{"id":"W3033936018","doi":"10.1063/5.0004641","title":"Material informatics for layered high-<i>T</i> <i>C</i> superconductors","year":2020,"lang":"en","type":"article","venue":"APL Materials","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Nanoacademic Technologies","funders":"National Natural Science Foundation of China","keywords":"Electronegativity; Superconductivity; Materials science; Cuprate; High-temperature superconductivity; Condensed matter physics; Atomic radius; Materials informatics; Machine learning; Computer science; Physics; Quantum mechanics; Health informatics","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":[0.001037758,0.0006588447,0.0006780302,0.001093917,0.0004105377,0.0008701311,0.001256514,0.0007010571,0.001941967],"category_scores_gemma":[0.002350247,0.0003705941,0.0009982483,0.0007047719,0.0006462516,0.001616939,0.0008309543,0.0009896618,0.0006022379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008830021,"about_ca_system_score_gemma":0.001035818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002512677,"about_ca_topic_score_gemma":0.003550408,"domain_scores_codex":[0.9997301,0.00006758146,0.00001832845,0.00007215136,0.00008504705,0.00002677497],"domain_scores_gemma":[0.9992747,0.0003027989,0.00009464459,0.0001520385,0.0001501633,0.00002556702],"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":[0.0001414756,0.0001425346,0.007827686,0.0002593903,0.00007356853,0.0001655901,0.00005589203,0.8729918,0.01320661,0.02151475,0.003010181,0.08061047],"study_design_scores_gemma":[0.000004588359,0.00002031612,0.0003986461,0.000006857047,0.000006523675,0.00001922894,0.000006986984,0.9878506,0.003661308,0.006984303,0.001035829,0.000004841538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2027461,0.001029711,0.7822906,0.0007430969,0.00007874431,0.0001016725,0.00188599,0.006188368,0.004935624],"genre_scores_gemma":[0.7072961,0.000424529,0.2863316,0.0001849569,0.0000455445,0.000167725,0.003775625,0.0002762055,0.001497588],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002512677,"threshold_uncertainty_score":0.006496549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01988715744010762,"score_gpt":0.2438974272501241,"score_spread":0.2240102698100165,"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."}}