{"id":"W4396906789","doi":"10.1016/j.trechm.2024.04.007","title":"Electrochemical energy conversion and storage processes with machine learning","year":2024,"lang":"en","type":"article","venue":"Trends in Chemistry","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"National Research Foundation of Korea; Ministry of Science, ICT and Future Planning","keywords":"Battery (electricity); Computer science; Artificial intelligence; Electrochemistry; Intersection (aeronautics); Electrolysis; Computation; Deep learning; Field (mathematics); Process engineering; Machine learning; Engineering; Chemistry; Electrode; Power (physics); Aerospace engineering; Algorithm; Mathematics","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":[0.0004422345,0.0002360482,0.0003048992,0.0002319994,0.0001808563,0.000860751,0.0006107739,0.0004809354,0.00247224],"category_scores_gemma":[0.001226107,0.0001797107,0.0002359963,0.0003757682,0.0003787644,0.002012868,0.0004852051,0.001113068,0.0004614798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004705371,"about_ca_system_score_gemma":0.0002680974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002478822,"about_ca_topic_score_gemma":0.0004355753,"domain_scores_codex":[0.9998152,0.00004331811,0.00001070819,0.00004028207,0.00007797498,0.00001262208],"domain_scores_gemma":[0.9997113,0.0001479605,0.00002564798,0.00005704413,0.00005014147,0.000007857827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002861245,0.0004161239,0.001617708,0.0008734143,0.0001324617,0.00009577103,0.0001271387,0.18419,0.06008774,0.1380536,0.005181461,0.6089385],"study_design_scores_gemma":[0.00002102337,0.00007298564,0.0003699309,0.00003594132,0.00001623624,0.0000586921,0.0000154802,0.8759974,0.06587163,0.04665571,0.01086828,0.00001673931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.136603,0.007832123,0.8132998,0.002510769,0.0006022417,0.0001430902,0.0002424959,0.001273365,0.03749309],"genre_scores_gemma":[0.7910625,0.003067735,0.1920129,0.0002971647,0.0002126061,0.00009622193,0.0002030426,0.00008002574,0.01296773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00247224,"threshold_uncertainty_score":0.008270502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006085537702999652,"score_gpt":0.2388531431205692,"score_spread":0.2327676054175696,"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."}}