{"id":"W4407386412","doi":"10.1002/anie.202424493","title":"Machine Learning‐Driven Mass Discovery and High‐Throughput Screening of Fluoroether‐Based Electrolytes for High‐Stability Lithium Metal Batteries","year":2025,"lang":"en","type":"article","venue":"Angewandte Chemie International Edition","topic":"Advanced Battery Materials and Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"Transformation Program of Scientific and Technological Achievements of Jiangsu Province; National Key Research and Development Program of China; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Electrolyte; Solvation; High voltage; Chemical space; Electrochemistry; Lithium (medication); Faraday efficiency; Materials science; Chemistry; Computer science; Solvent; Chemical engineering; Nanotechnology; Voltage; Drug discovery; Electrode; Physics; Organic chemistry","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":[0.00009138686,0.0001510064,0.0002110574,0.00009232068,0.00005242071,0.00004869791,0.0001222107,0.00008896,0.00005052835],"category_scores_gemma":[0.0001197414,0.0001445655,0.00005140538,0.00007082656,0.00009954157,0.0003840914,0.00004102322,0.0001142967,4.015536e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005533717,"about_ca_system_score_gemma":0.000009071614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003684021,"about_ca_topic_score_gemma":0.000009200012,"domain_scores_codex":[0.9992963,0.0000100597,0.0002394939,0.0001958114,0.0001168411,0.0001414835],"domain_scores_gemma":[0.999554,0.0001669201,0.00007452576,0.0001067775,0.00008444388,0.0000132721],"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":[0.0001155053,0.0000237707,0.001022493,0.0002059898,0.0001331575,8.18859e-7,0.00002266884,0.001857518,0.992595,0.002417638,0.0002172806,0.001388149],"study_design_scores_gemma":[0.0005275463,0.00005965139,0.0009941624,0.00007819062,0.00003145051,7.827696e-7,0.00003485672,0.003105318,0.9855118,0.007888241,0.001644433,0.0001235289],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3797128,0.0002729744,0.6167492,0.001183625,0.0008509718,0.0001906792,0.0005297685,0.0002890113,0.0002210426],"genre_scores_gemma":[0.9897018,0.0001027484,0.009153517,0.00004787212,0.0001800125,0.00008941208,0.0006274615,0.00001789582,0.00007925751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.609989,"threshold_uncertainty_score":0.5895211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009617869984133018,"score_gpt":0.2295574426026361,"score_spread":0.2199395726185031,"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."}}