{"id":"W4400763347","doi":"10.1016/j.est.2024.112914","title":"Predictive modeling for hydrogen storage in functionalized carbonaceous nanomaterials using machine learning","year":2024,"lang":"en","type":"article","venue":"Journal of Energy Storage","topic":"Hybrid Renewable Energy Systems","field":"Energy","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Hydrogen storage; Nanomaterials; Computer science; Hydrogen; Nanotechnology; Materials science; Machine learning; Process engineering; Chemistry; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004640741,0.0003980926,0.0006141402,0.0003104406,0.0003276377,0.000540627,0.0005987926,0.000671858,0.001179019],"category_scores_gemma":[0.001138605,0.0002604034,0.000498068,0.0003123569,0.0003490464,0.0007436412,0.0002644367,0.0007105299,0.0001546181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007816506,"about_ca_system_score_gemma":0.0005144831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009729127,"about_ca_topic_score_gemma":0.008129594,"domain_scores_codex":[0.999916,0.0000221204,0.00000496131,0.00001851606,0.0000216641,0.00001670711],"domain_scores_gemma":[0.9994306,0.0004235384,0.00003521467,0.0000291772,0.00006868441,0.00001274467],"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":[0.0000300035,0.00002343435,0.0002847516,0.00001699152,0.00001214718,0.00001500784,0.000005500218,0.995199,0.0007887708,0.0007901894,0.0001146848,0.002719477],"study_design_scores_gemma":[5.310196e-7,0.000001943499,0.00002665127,3.910194e-7,6.128527e-7,5.615439e-7,5.901442e-7,0.9996305,0.0001865218,0.0001393806,0.00001176024,5.477459e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6665544,0.00137113,0.3226593,0.0007794983,0.0001441957,0.00007111468,0.0004812032,0.0006340973,0.007304996],"genre_scores_gemma":[0.9949942,0.000111217,0.003594183,0.00002618535,0.00001186521,0.00002593439,0.0001044576,0.00001316554,0.001118791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009729127,"threshold_uncertainty_score":0.01934499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02233670148212623,"score_gpt":0.2513088227549671,"score_spread":0.2289721212728408,"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."}}