{"id":"W4389922190","doi":"10.1038/s41598-023-50044-0","title":"Data-driven analysis and prediction of stable phases for high-entropy alloy design","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"High Entropy Alloys Studies","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Calgary","keywords":"Computer science; High entropy alloys; Random forest; Feature selection; Representation (politics); Alloy; Phase (matter); Machine learning; Artificial intelligence; Feature (linguistics); Entropy (arrow of time); Data mining; Materials science; Thermodynamics; Chemistry","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":[0.0008710719,0.0001089287,0.0002605628,0.0003823748,0.0001687909,0.00008531275,0.0001209844,0.00003369377,0.00002961004],"category_scores_gemma":[0.0001696469,0.0001018829,0.00004913746,0.001206747,0.0001104513,0.0002391875,0.00009864195,0.00003525165,0.000003298537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002765131,"about_ca_system_score_gemma":0.00002021389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002239912,"about_ca_topic_score_gemma":0.00003528315,"domain_scores_codex":[0.99858,0.00001762077,0.0003957092,0.000463774,0.0002884179,0.0002544119],"domain_scores_gemma":[0.9988973,0.00009751806,0.0001024926,0.0007428213,0.0001032317,0.00005663982],"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":[0.00001807396,0.00006039517,0.01501465,0.0002069488,0.002038611,0.00009597864,0.0004969718,0.3927387,0.3357861,0.0001543735,0.2527698,0.0006194156],"study_design_scores_gemma":[0.0004699423,0.0000731426,0.01682027,0.00004226302,0.001604294,0.00001642764,0.0001962653,0.8605713,0.08579218,0.002431415,0.03165809,0.0003244422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8007971,0.0003204186,0.1918097,0.00004916057,0.004792021,0.0007587727,0.0006759143,0.0007314317,0.00006549304],"genre_scores_gemma":[0.990617,0.00006981883,0.007261211,0.000001086858,0.00004946247,0.00004974889,0.0009743451,0.00001916268,0.0009581407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4678326,"threshold_uncertainty_score":0.4154663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04313479806599042,"score_gpt":0.2609703190276985,"score_spread":0.2178355209617081,"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."}}