{"id":"W4413256406","doi":"10.3390/chemengineering9040085","title":"Graph Neural Networks for Sustainable Energy: Predicting Adsorption in Aromatic Molecules","year":2025,"lang":"en","type":"article","venue":"ChemEngineering","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Computer science; Machine learning; Artificial intelligence; Domain (mathematical analysis); Retraining; Process (computing); Transfer of learning; Artificial neural network; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0006168478,0.0001483113,0.0001836813,0.0001914653,0.00007652253,0.0001496047,0.0002743109,0.00005835579,0.0000252748],"category_scores_gemma":[0.0003054824,0.0001534147,0.00003830523,0.0004110212,0.00003028755,0.0002441244,0.0001050392,0.00007992434,0.000001032118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008055818,"about_ca_system_score_gemma":0.00002148685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007054607,"about_ca_topic_score_gemma":0.000007658035,"domain_scores_codex":[0.998774,0.00002442527,0.0002944976,0.0002937731,0.0001154715,0.0004978262],"domain_scores_gemma":[0.9995503,0.0001106,0.00006710843,0.0001968238,0.0000359995,0.00003911019],"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":[0.000009014418,0.000009936693,0.0006119424,0.0001699625,0.000001982076,0.000002912926,0.0000449026,0.6666189,0.32927,0.002981962,0.00004464992,0.000233843],"study_design_scores_gemma":[0.0002685068,0.00002067486,0.0008518726,0.0000899168,0.000006446822,0.00000154419,0.00008937894,0.9366108,0.06026902,0.001469223,0.0001845197,0.0001381231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6071367,0.0001735986,0.3916633,0.00006212051,0.0003265632,0.0001641778,6.062195e-7,0.0001645754,0.0003083695],"genre_scores_gemma":[0.9932753,0.000004989155,0.006089794,0.0000597828,0.00007020401,0.0001705706,0.000006935778,0.00001651808,0.0003059431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3861385,"threshold_uncertainty_score":0.6256072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004370364670499954,"score_gpt":0.2232137516010014,"score_spread":0.2188433869305015,"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."}}