{"id":"W4413516768","doi":"10.1021/acs.energyfuels.5c02941","title":"Predictive Modeling of CO<sub>2</sub> Adsorption in Metal–Organic Frameworks Using Hybrid Machine Learning Approaches","year":2025,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Metal-Organic Frameworks: Synthesis and Applications","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Adsorption; Metal-organic framework; Computer science; Artificial intelligence; Materials science; Chemistry; Machine learning; Process engineering; Environmental science; Organic chemistry; Engineering","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":[0.0005125111,0.0005824463,0.0006279494,0.0003769193,0.0002119878,0.0005850967,0.0005918945,0.0007771708,0.0007866769],"category_scores_gemma":[0.0008441419,0.0002831484,0.0006601624,0.0003739059,0.0003188104,0.0004720152,0.0002916338,0.0004671187,0.0001183079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008479737,"about_ca_system_score_gemma":0.0005417203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01364688,"about_ca_topic_score_gemma":0.009748916,"domain_scores_codex":[0.999871,0.00004212438,0.000007606133,0.00002483164,0.00003405476,0.00002023704],"domain_scores_gemma":[0.9994393,0.0004131274,0.00004972245,0.00001418331,0.00007417022,0.000009418738],"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":[0.00002388969,0.00001924021,0.0003984381,0.00002024741,0.00001584518,0.0000146277,0.000005984391,0.9957023,0.0008109439,0.0001899129,0.00004017774,0.00275841],"study_design_scores_gemma":[6.721419e-7,0.000003768518,0.0000547519,4.698325e-7,8.512202e-7,8.055941e-7,0.000001307591,0.9997283,0.0001559008,0.00003678254,0.00001563627,6.963317e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7541167,0.001248588,0.2362542,0.0004194659,0.00005046721,0.00007752861,0.0004276295,0.0006032608,0.006802222],"genre_scores_gemma":[0.9890932,0.0001633874,0.009653942,0.0000244645,0.000006305272,0.00005085193,0.0000911442,0.00001004754,0.0009067745],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01364688,"threshold_uncertainty_score":0.0271349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02147040448363554,"score_gpt":0.2338648886279276,"score_spread":0.212394484144292,"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."}}