{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002227591,0.0003400558,0.0003293203,0.0004114333,0.0002789776,0.0003327438,0.0008159878,0.0004982902,0.001477529],"category_scores_gemma":[0.0007141733,0.0001788607,0.0002164718,0.0003019462,0.0002767077,0.00089423,0.0003361265,0.0005740671,0.0004161968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005668349,"about_ca_system_score_gemma":0.0003841906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001482506,"about_ca_topic_score_gemma":0.001837954,"domain_scores_codex":[0.9999589,0.000007462355,0.000002205016,0.000009269018,0.0000185946,0.000003701394],"domain_scores_gemma":[0.9998025,0.0001033081,0.00002602493,0.0000219707,0.00003537345,0.00001078245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000485514,0.0000839322,0.001528889,0.00007618951,0.00002257374,0.00003979742,0.00002070517,0.9342363,0.01584043,0.006066363,0.0006062418,0.04142998],"study_design_scores_gemma":[6.501566e-7,0.000003064369,0.00005427141,7.240097e-7,4.858834e-7,0.000001777511,0.000001205443,0.9978254,0.001114009,0.0009110646,0.00008637128,0.000001017166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1635567,0.001060266,0.8237365,0.0005945198,0.00007354141,0.00006332125,0.0003473909,0.00274419,0.007823544],"genre_scores_gemma":[0.9099824,0.0002689721,0.08803712,0.00008581311,0.00002635149,0.00008279621,0.00020971,0.00006532216,0.001241481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001482506,"threshold_uncertainty_score":0.004942834,"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."}}