{"id":"W4413954097","doi":"10.1039/d5ta05426e","title":"Generalizable classification of crystal structure error types using graph attention networks","year":2025,"lang":"en","type":"article","venue":"Journal of Materials Chemistry A","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Total; Alliance de recherche numérique du Canada; Mitacs; Canada Foundation for Innovation; University of Ottawa","keywords":"Computer science; Artificial intelligence; Graph; Pattern recognition (psychology); Theoretical computer 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":[0.001480006,0.0009469891,0.000866061,0.002246148,0.0003065729,0.0009909576,0.001415132,0.001165779,0.001101206],"category_scores_gemma":[0.00786545,0.0002516422,0.0005461386,0.001290833,0.0004708302,0.001662285,0.0008545964,0.001213588,0.0002904886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001224036,"about_ca_system_score_gemma":0.0007396378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01418883,"about_ca_topic_score_gemma":0.01812637,"domain_scores_codex":[0.9994158,0.0001410106,0.00003860218,0.0001969435,0.0001364428,0.00007126661],"domain_scores_gemma":[0.9956253,0.002509446,0.0006669322,0.0004826391,0.0006069377,0.0001087241],"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":[0.0003637677,0.0003422762,0.02356313,0.0001160105,0.0001739371,0.0001330079,0.00007483127,0.7426537,0.005593998,0.005233429,0.006188903,0.2155631],"study_design_scores_gemma":[0.000004065645,0.000009676321,0.0007089908,0.00000249365,0.000005125986,0.000005789021,0.000004525108,0.9956845,0.0005254779,0.002992479,0.00005359438,0.000003316597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5946016,0.0007395889,0.3954852,0.0008081552,0.00009697372,0.0001420111,0.001557431,0.003456007,0.003113044],"genre_scores_gemma":[0.9576781,0.0001710844,0.03870757,0.0001380532,0.00004805469,0.00004804836,0.001892883,0.00008406414,0.001232178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01418883,"threshold_uncertainty_score":0.02821249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01553909280166208,"score_gpt":0.2812368315697714,"score_spread":0.2656977387681093,"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."}}