{"id":"W2992741529","doi":"10.1021/acsaem.9b01887","title":"Accelerated Screening of High-Energy Lithium-Ion Battery Cathodes","year":2019,"lang":"en","type":"article","venue":"ACS Applied Energy Materials","topic":"Advancements in Battery Materials","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Electrochemistry; Cathode; Battery (electricity); Lithium (medication); Materials science; Throughput; High energy; Lithium-ion battery; Reproducibility; Electrochemical cell; Energy storage; Electrochemical energy storage; Ion; Lithium battery; Electrode; Nanotechnology; Chemical engineering; Computer science; Engineering physics; Chemistry; Physical chemistry; Physics; Engineering; Power (physics); Telecommunications; Thermodynamics; Chromatography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006181888,0.0005129324,0.0004065601,0.0004217918,0.0002555928,0.0009861393,0.0006817124,0.0005372551,0.0009659409],"category_scores_gemma":[0.0009453028,0.0001727009,0.0002365868,0.0003006851,0.0001924355,0.0006830401,0.0005999117,0.0004278025,0.0004471044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003802863,"about_ca_system_score_gemma":0.0002618686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004726248,"about_ca_topic_score_gemma":0.00139907,"domain_scores_codex":[0.9994385,0.00008085859,0.00002840172,0.00009495432,0.0003012153,0.00005620806],"domain_scores_gemma":[0.9997248,0.0000858031,0.00002641045,0.00002799868,0.0001032478,0.00003172553],"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":[0.00005351937,0.00006280411,0.0008185079,0.00009054303,0.000008990925,0.000109492,0.00003424224,0.0005793508,0.9916338,0.0002786614,0.0001647147,0.006165422],"study_design_scores_gemma":[0.00000975063,0.0001368806,0.001058292,0.00000538198,0.000007181537,0.0001166798,0.00003937119,0.004272613,0.9925197,0.0001365929,0.001689318,0.000008200172],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.965071,0.001503342,0.02763882,0.0002674488,0.00004582449,0.0001956768,0.0007181718,0.0004081787,0.004151626],"genre_scores_gemma":[0.9711334,0.001379352,0.02436618,0.00008737952,0.00002252248,0.0001104859,0.0008038721,0.00006790162,0.0020289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009861393,"threshold_uncertainty_score":0.003269315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01396266786264963,"score_gpt":0.2180370366835199,"score_spread":0.2040743688208702,"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."}}