{"id":"W4405474883","doi":"10.1021/acsami.4c19149","title":"Thermoelectric Material Performance (<i>zT</i>) Predictions with Machine Learning","year":2024,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Thermoelectric Materials and Devices","field":"Materials Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Thermoelectric effect; Thermoelectric materials; Engineering physics; Nanotechnology; Composite material; Thermal conductivity; Thermodynamics","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":[0.0006227731,0.0007476342,0.0003301735,0.0004937711,0.0001504467,0.0005004854,0.0005358247,0.0006232561,0.001468853],"category_scores_gemma":[0.002432366,0.0002656876,0.000526071,0.0006302933,0.0003097316,0.0008009684,0.000263341,0.0008381123,0.0005605972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007280851,"about_ca_system_score_gemma":0.0004018567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002593312,"about_ca_topic_score_gemma":0.003724604,"domain_scores_codex":[0.9998251,0.00003261135,0.000009758964,0.00005269461,0.00006348865,0.00001631824],"domain_scores_gemma":[0.9993433,0.0004156059,0.00006085081,0.00007975858,0.0000906274,0.000009806325],"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":[0.00006915758,0.00008400241,0.006038299,0.0001575727,0.00005758912,0.00006018539,0.00002820343,0.9371917,0.02004992,0.002035517,0.001613599,0.03261426],"study_design_scores_gemma":[0.000003794865,0.00002105294,0.000936149,0.000007925767,0.000005235745,0.00001338278,0.000005403646,0.9845645,0.01272459,0.00114792,0.0005627014,0.000007338425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6378506,0.001138959,0.3436221,0.0007414346,0.0001123837,0.00007023987,0.003599697,0.003132995,0.009731635],"genre_scores_gemma":[0.9616409,0.0003859066,0.03510369,0.00006542883,0.00001647257,0.00006869968,0.001506775,0.0001179623,0.001094231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002593312,"threshold_uncertainty_score":0.00528264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006089146472211548,"score_gpt":0.2098493822298242,"score_spread":0.2037602357576126,"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."}}