{"id":"W2510169513","doi":"10.1021/acs.chemmater.6b02905","title":"Classifying Crystal Structures of Binary Compounds AB through Cluster Resolution Feature Selection and Support Vector Machine Analysis","year":2016,"lang":"en","type":"article","venue":"Chemistry of Materials","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Alberta; Genome Canada","keywords":"Support vector machine; Linear discriminant analysis; Artificial intelligence; Valence (chemistry); Pattern recognition (psychology); Crystal structure; Feature selection; Chemistry; Binary number; Computer science; Mathematics; Crystallography","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":[0.001484541,0.000704359,0.0009004403,0.002099733,0.0003823693,0.0008956263,0.0005904898,0.0004095645,0.001190297],"category_scores_gemma":[0.002821746,0.0002521004,0.0006947771,0.001636076,0.0003376109,0.0005878197,0.000428006,0.0006364296,0.0005159461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005795457,"about_ca_system_score_gemma":0.0009768612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003823163,"about_ca_topic_score_gemma":0.00328696,"domain_scores_codex":[0.999379,0.0001435957,0.00005240035,0.0001664457,0.0002084963,0.00005001053],"domain_scores_gemma":[0.9987985,0.0005360838,0.0001431561,0.00009180333,0.0003919746,0.00003850347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001799273,0.0007557495,0.05509495,0.000573815,0.0002418817,0.0003395263,0.0002702931,0.09929339,0.2382818,0.004748647,0.003760534,0.5948401],"study_design_scores_gemma":[0.00005182886,0.0002526278,0.01514235,0.00001300112,0.0000481542,0.0001170307,0.00007783536,0.9386213,0.04211794,0.001922132,0.001601129,0.00003462362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6509819,0.0004536207,0.3416915,0.0002079303,0.00003232505,0.0004477528,0.001935688,0.002486291,0.001762893],"genre_scores_gemma":[0.7281598,0.0001695772,0.2675709,0.00003640206,0.00001477065,0.0003330143,0.00272974,0.00008743034,0.0008984067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003823163,"threshold_uncertainty_score":0.007851124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01106432125611888,"score_gpt":0.2605531895649865,"score_spread":0.2494888683088676,"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."}}