{"id":"W2520500207","doi":"10.1021/acs.chemmater.6b02724","title":"High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds","year":2016,"lang":"en","type":"article","venue":"Chemistry of Materials","topic":"Heusler alloys: electronic and magnetic properties","field":"Materials Science","cited_by":395,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Directorate-General for Research and Innovation; European Commission","keywords":"Heusler compound; Flagging; Materials science; Spintronics; Intermetallic; Crystallography; Condensed matter physics; Nanotechnology; Physics; Chemistry; Alloy; Metallurgy; Metal","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":[0.0005088969,0.0006451464,0.0007316501,0.0005240161,0.0003751994,0.0005679541,0.0005844401,0.0005270671,0.001888967],"category_scores_gemma":[0.001054795,0.0003729481,0.0004535814,0.00063841,0.0002429658,0.0004088984,0.0002747668,0.0005934468,0.000613788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007265693,"about_ca_system_score_gemma":0.0009773364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002040025,"about_ca_topic_score_gemma":0.004677312,"domain_scores_codex":[0.9998129,0.00002623307,0.00001073874,0.00006200026,0.00006389685,0.00002422077],"domain_scores_gemma":[0.9996582,0.0001933085,0.00003295327,0.00004359925,0.00005136855,0.00002048642],"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":[0.0004933131,0.0004692584,0.003356667,0.0006094452,0.0001219441,0.0003776549,0.0001130127,0.6400093,0.2212848,0.006337133,0.003117804,0.1237098],"study_design_scores_gemma":[0.00002835977,0.0001103686,0.0002419268,0.0000036778,0.00001299086,0.00002520701,0.00001158695,0.9236679,0.07376216,0.0008700485,0.001256766,0.0000090188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8061506,0.001155076,0.1757507,0.0003938435,0.00007329423,0.0003222096,0.002996341,0.005560922,0.007597019],"genre_scores_gemma":[0.8563042,0.0003404423,0.1384106,0.00005169681,0.00001128142,0.0001661121,0.002142654,0.0001886664,0.002384227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002040025,"threshold_uncertainty_score":0.006319225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009128184869654605,"score_gpt":0.2046243991878898,"score_spread":0.1954962143182352,"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."}}