{"id":"W7083592679","doi":"10.1016/j.est.2025.118664","title":"Chemical space exploration for SSEs: Data-driven insights into structure-property relationships","year":2025,"lang":"en","type":"article","venue":"Journal of Energy Storage","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Chemical space; Key (lock); Feature (linguistics); Bayesian optimization; Bayesian network; Generative grammar; Bayesian probability; Generative adversarial network","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":[0.0002267217,0.0001035513,0.0002214995,0.0001196216,0.0001445155,0.00004932414,0.0003692783,0.00008947541,0.0000612754],"category_scores_gemma":[0.0006457365,0.00005959568,0.00006739174,0.0003728964,0.00003747663,0.0005815971,0.00002202161,0.0002420317,0.000003054527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001111101,"about_ca_system_score_gemma":0.0001099791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001934453,"about_ca_topic_score_gemma":0.0003337,"domain_scores_codex":[0.9990175,0.00016884,0.0003089146,0.0001709212,0.0001934696,0.0001403917],"domain_scores_gemma":[0.9988554,0.0005083599,0.0001821352,0.0002029657,0.0001479817,0.000103168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001292953,0.0002672929,0.005264296,0.0001578026,0.0004348533,0.00005747372,0.0009149082,0.0962045,0.01817946,0.02710561,0.04308353,0.8070373],"study_design_scores_gemma":[0.001407296,0.0006589751,0.02863077,0.0001125683,0.0002054596,0.00001761668,0.0002366212,0.1896437,0.01042927,0.3022858,0.4659095,0.0004624339],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5040747,0.00253035,0.4823987,0.005855692,0.002045531,0.0002093972,0.00005745307,0.00003866574,0.002789569],"genre_scores_gemma":[0.9708873,0.00004398036,0.02784391,0.0001943392,0.0003834404,4.936604e-7,0.00009908532,0.000002543879,0.0005448819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8065749,"threshold_uncertainty_score":0.2430241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0448381382351653,"score_gpt":0.2698744460597416,"score_spread":0.2250363078245763,"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."}}