{"id":"W4408253332","doi":"10.1016/j.jpowsour.2025.236709","title":"Optimization of catholyte for halide-based all-solid-state batteries","year":2025,"lang":"en","type":"article","venue":"Journal of Power Sources","topic":"Advanced Battery Materials and Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Saint-Gobain (Canada)","funders":"Lawrence Berkeley National Laboratory; Vehicle Technologies Office; Office of Energy Efficiency; Basic Energy Sciences; U.S. Department of Energy; Office of Energy Efficiency and Renewable Energy; Advanced Materials and Manufacturing Technologies Office; Office of Science; University of California","keywords":"Halide; Chemistry; Solid-state; State (computer science); Materials science; Chemical engineering; Combinatorial chemistry; Inorganic chemistry; Computer science; Engineering; Physical chemistry; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000236407,0.0004780076,0.0005565032,0.000281243,0.0003073094,0.00106939,0.0006498054,0.0003998147,0.002685094],"category_scores_gemma":[0.0003808861,0.0002085521,0.0002656486,0.000405877,0.0001547356,0.0007199346,0.0005166634,0.0004531067,0.0007438435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000593462,"about_ca_system_score_gemma":0.0006764179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001164875,"about_ca_topic_score_gemma":0.004084476,"domain_scores_codex":[0.9998749,0.00001296213,0.00001081196,0.00002256344,0.00005389652,0.0000247367],"domain_scores_gemma":[0.9999056,0.00002122021,0.00001263684,0.000006358729,0.00004014209,0.00001405319],"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":[0.001164634,0.0004492074,0.0008985318,0.001302832,0.00006735538,0.0002612105,0.00007466575,0.03133071,0.9306602,0.002933027,0.001001453,0.02985617],"study_design_scores_gemma":[0.0000716202,0.000911335,0.0005851631,0.00003586894,0.00004344192,0.00009191621,0.0001033801,0.05726242,0.9322473,0.0004531724,0.008166392,0.00002810184],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734707,0.003050861,0.01316866,0.0003055993,0.00006920855,0.00009850958,0.0005730616,0.0001844481,0.009078997],"genre_scores_gemma":[0.9882373,0.001854277,0.006860671,0.00003677681,0.00000783134,0.00008378857,0.0003667073,0.00006709571,0.002485562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002685094,"threshold_uncertainty_score":0.008982539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007569984621154061,"score_gpt":0.2465982943587564,"score_spread":0.2390283097376024,"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."}}