{"id":"W4407960750","doi":"10.1088/2515-7655/adba87","title":"Recent strides in artificial intelligence for predicting thermoelectric properties and materials discovery","year":2025,"lang":"en","type":"article","venue":"Journal of Physics Energy","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Materials science; Machine learning; Data science","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":[0.008541564,0.0008000424,0.0009554347,0.002094462,0.0004380925,0.003903814,0.001558872,0.00182895,0.001964971],"category_scores_gemma":[0.01469692,0.0005255711,0.0007085213,0.002373968,0.002437516,0.004968017,0.001970914,0.003583655,0.0008011388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00115099,"about_ca_system_score_gemma":0.000995645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001013668,"about_ca_topic_score_gemma":0.001210078,"domain_scores_codex":[0.9984177,0.0007880764,0.00008602433,0.0002647609,0.0003725397,0.00007081556],"domain_scores_gemma":[0.9827283,0.01352319,0.0008047979,0.001103134,0.001462704,0.0003777817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003460593,0.0003411377,0.01764697,0.002378702,0.0004548972,0.000224408,0.0003532592,0.1690187,0.004403676,0.3405519,0.02184816,0.4424321],"study_design_scores_gemma":[0.00003146203,0.00011514,0.003246286,0.0005214898,0.00009375896,0.0001917219,0.0002505889,0.5928483,0.003799694,0.3420162,0.05679731,0.00008803212],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.1156686,0.3288257,0.360631,0.1410318,0.002040198,0.00008393281,0.0006738826,0.001246547,0.04979825],"genre_scores_gemma":[0.658673,0.1575665,0.1712069,0.003892294,0.003709276,0.00009785506,0.0006123587,0.000192007,0.004049737],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.008541564,"threshold_uncertainty_score":0.04517269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0302776388990595,"score_gpt":0.2709792108250261,"score_spread":0.2407015719259666,"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."}}