{"id":"W2805504266","doi":"10.1038/s41467-018-05169-6","title":"Insightful classification of crystal structures using deep learning","year":2018,"lang":"en","type":"article","venue":"Nature Communications","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":340,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Waterloo","funders":"Horizon 2020; European Commission","keywords":"Deep learning; Artificial neural network; Construct (python library); Pattern recognition (psychology); Crystal structure; Identification (biology); Deep neural networks; Lattice (music)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006772766,0.0001249749,0.0001743073,0.0001287392,0.0006321177,0.00009227055,0.001606028,0.0002019907,0.0004780365],"category_scores_gemma":[0.0008977549,0.0001128718,0.00004063566,0.0004101384,0.000810037,0.0002554209,0.0004781625,0.0005424097,0.00005033952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005307102,"about_ca_system_score_gemma":0.00006944984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007131761,"about_ca_topic_score_gemma":0.0001905197,"domain_scores_codex":[0.998412,0.0004747752,0.0003478801,0.0002517842,0.0003101279,0.0002034822],"domain_scores_gemma":[0.997534,0.0002227968,0.0003817055,0.001397839,0.0004094747,0.00005422157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009046491,0.00002388283,0.003941774,0.000008899568,0.0000028804,1.069029e-7,0.0006128383,0.0003972381,0.9756141,0.01899508,0.00004933191,0.0003448066],"study_design_scores_gemma":[0.0005820548,0.0002888315,0.2690003,0.0001181059,0.00008566417,0.0000577428,0.001020895,0.2588466,0.4229417,0.004878666,0.04141084,0.0007685267],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877675,0.0006963104,0.005773503,0.0003483981,0.0005270811,0.0001537611,0.000008756823,0.0001363385,0.004588314],"genre_scores_gemma":[0.9018958,0.00002026183,0.09783689,0.0000659385,0.0001121177,0.000005723798,0.00001765514,0.00001501487,0.00003056776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5526724,"threshold_uncertainty_score":0.5234165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03610897137876738,"score_gpt":0.3432417308081047,"score_spread":0.3071327594293373,"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."}}