{"id":"W4401684476","doi":"10.1021/acs.jpcc.4c01221","title":"Machine Learning for High-Throughput Configuration Sampling of Li−La−Ti−O Disordered Solid-State Electrolyte","year":2024,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry C","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Electrolyte; Throughput; Solid-state; Sampling (signal processing); Materials science; Computer science; High-throughput screening; Chemical engineering; Chemistry; Nanotechnology; Engineering; Physical chemistry; Electrode; Telecommunications; Biochemistry","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":[0.001565463,0.0001664769,0.0003496724,0.00002553021,0.0001438684,0.00015187,0.0004829925,0.00004291573,0.000155889],"category_scores_gemma":[0.0005388214,0.0001075502,0.0001273805,0.0001550749,0.0001958688,0.0002272441,0.00006550785,0.0003883695,0.00001196639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004157102,"about_ca_system_score_gemma":0.0001218752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002409541,"about_ca_topic_score_gemma":5.805423e-7,"domain_scores_codex":[0.9985011,0.0001693017,0.0004865138,0.0001766947,0.0004015519,0.0002648494],"domain_scores_gemma":[0.998147,0.0009980039,0.0004627716,0.000170303,0.0001608082,0.00006109332],"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":[0.0001582964,0.00004202478,0.000005051951,0.0002267486,0.00001805104,0.000001913077,0.0006517947,0.0496177,0.9481628,0.0002798111,0.00003220257,0.0008036353],"study_design_scores_gemma":[0.0002619457,0.0002043584,0.00004003541,0.0001331831,0.00006241348,0.00005012525,0.00003390872,0.06627064,0.9246191,0.00762849,0.0005811374,0.0001145936],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9426526,0.0002415894,0.05609775,0.0004855398,0.0001635491,0.00009845983,0.0000180158,0.00004331949,0.0001991227],"genre_scores_gemma":[0.9981125,0.00002983549,0.00107376,0.00001543661,0.0004769951,0.000003828905,0.000005876873,0.00002419374,0.0002575981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05545982,"threshold_uncertainty_score":0.4385769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01068762491833248,"score_gpt":0.2943145867202381,"score_spread":0.2836269618019057,"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."}}