{"id":"W4391360926","doi":"10.3390/batteries10020051","title":"AI-Based Nano-Scale Material Property Prediction for Li-Ion Batteries","year":2024,"lang":"en","type":"article","venue":"Batteries","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Molecular dynamics; Range (aeronautics); Property (philosophy); Particle (ecology); Scale (ratio); Work (physics); Interatomic potential; Ion; Statistical physics; Materials science; Computational science; Computational chemistry; Physics; Chemistry; Thermodynamics; Quantum mechanics","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":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009134987,0.0003322151,0.0003321123,0.0001443466,0.0004392525,0.001575783,0.000454174,0.0001363946,0.00413777],"category_scores_gemma":[0.0001194434,0.0002269079,0.0000996441,0.0001887464,0.0004381468,0.000921255,0.0001444608,0.0001084172,0.0005419238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008497895,"about_ca_system_score_gemma":0.000170346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001044978,"about_ca_topic_score_gemma":0.00002446699,"domain_scores_codex":[0.9975165,0.0001771888,0.0005058591,0.0007987458,0.0004296746,0.0005720295],"domain_scores_gemma":[0.9991264,0.0001155365,0.0001009337,0.0004453309,0.0001097049,0.0001020883],"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":[0.0002171356,0.00003086177,0.0003698913,0.0005897981,0.000004361304,0.000005928463,0.0004446497,0.0003566864,0.9795246,0.0001679034,0.01761549,0.0006727719],"study_design_scores_gemma":[0.0002449352,0.0003665924,0.0008445962,0.0002466941,0.00003005589,0.00002731714,0.00005594351,0.007588594,0.8218243,0.0004501959,0.1679721,0.0003486612],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9321175,0.00006446907,0.04004633,0.009244194,0.01441985,0.000944741,0.001262353,0.001494567,0.0004060527],"genre_scores_gemma":[0.9804115,0.000005785919,0.01187232,0.001641169,0.001632578,0.000685416,0.0002128588,0.00009921753,0.003439127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1577002,"threshold_uncertainty_score":0.9994607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01191640981257199,"score_gpt":0.2563320027958752,"score_spread":0.2444155929833032,"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."}}