{"id":"W3206993359","doi":"10.33774/chemrxiv-2021-s0vqp","title":"Finite Element Modeling of the Dielectric Response of Metal/Metal Oxide Nanocomposites: Coarse-Graining the Quantum Response","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Dielectric materials and actuators","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Materials science; Nanocomposite; Dielectric; Ab initio; Finite element method; Oxide; Granularity; Composite material; Nanoscopic scale; Nanotechnology; Chemical physics; Condensed matter physics; Physics; Computer science; Optoelectronics; Thermodynamics; Quantum mechanics; Metallurgy","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":[0.0001886085,0.000294249,0.0002890873,0.0002858306,0.000244999,0.0004551798,0.0006968257,0.001072571,0.001080136],"category_scores_gemma":[0.000691617,0.0002485616,0.0003681922,0.0002225561,0.0006131597,0.0004333271,0.0003377863,0.0005255984,0.0001634553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004627502,"about_ca_system_score_gemma":0.0005351828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004553947,"about_ca_topic_score_gemma":0.00450358,"domain_scores_codex":[0.9999344,0.00001741255,0.000003077618,0.00001131738,0.00002354233,0.00001013503],"domain_scores_gemma":[0.9998198,0.000104312,0.0000204551,0.00002335159,0.00002091258,0.00001118259],"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":[0.00001608914,0.00002906145,0.0003701673,0.00002071409,0.000005629962,0.00003099425,0.00003175583,0.9829028,0.01034132,0.00440756,0.00007819966,0.001765635],"study_design_scores_gemma":[0.000001731377,0.000002546717,0.00004452311,8.966842e-7,6.301186e-7,0.000002640279,0.000003191525,0.9988614,0.0006185962,0.0003637172,0.00009905334,0.000001175227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4532343,0.0002647724,0.5294768,0.0005141954,0.00005097812,0.00008266347,0.0002440827,0.0005032862,0.01562879],"genre_scores_gemma":[0.9314616,0.0001330668,0.06427484,0.00005450967,0.00001139188,0.00008672394,0.00009974992,0.00008727617,0.003790889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004553947,"threshold_uncertainty_score":0.009054899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01866557428519282,"score_gpt":0.230493643428377,"score_spread":0.2118280691431842,"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."}}