{"id":"W4386799679","doi":"10.1016/j.comptc.2023.114332","title":"Machine learning estimation of reaction energy barriers","year":2023,"lang":"en","type":"article","venue":"Computational and Theoretical Chemistry","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Chemistry; Mean squared error; Kernel (algebra); Regression; Density functional theory; Laplace operator; Computational chemistry; Statistics; Combinatorics; Quantum mechanics; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001351156,0.0005647186,0.001067058,0.00111944,0.0004807255,0.001234994,0.001534795,0.001323576,0.002125714],"category_scores_gemma":[0.006704778,0.0005840668,0.0007089881,0.0005821047,0.0005781518,0.001632691,0.0007218049,0.001819188,0.0005414909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001066728,"about_ca_system_score_gemma":0.001156898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003193246,"about_ca_topic_score_gemma":0.002673124,"domain_scores_codex":[0.9996827,0.0001217993,0.0000140892,0.00008128508,0.00006140356,0.00003881507],"domain_scores_gemma":[0.9964961,0.002792129,0.0002444261,0.0001746235,0.0002193919,0.00007322873],"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":[0.0001647662,0.000134479,0.0009603768,0.0001236539,0.00003921771,0.00003354318,0.00002473427,0.9375865,0.002714079,0.01953628,0.001024264,0.03765802],"study_design_scores_gemma":[0.000004020795,0.000003421724,0.00005426759,0.000002310263,0.000001445593,0.000002182215,0.00000126419,0.996003,0.0003710579,0.003498971,0.00005648945,0.000001598834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1835691,0.001471344,0.805559,0.0009650293,0.0001233418,0.00008505679,0.0003534795,0.001704775,0.00616885],"genre_scores_gemma":[0.9003443,0.0003443645,0.09584849,0.000116749,0.00005745378,0.0001223526,0.0004561386,0.0001591024,0.002551065],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003193246,"threshold_uncertainty_score":0.007739723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004514631142913617,"score_gpt":0.2333025147270763,"score_spread":0.2287878835841627,"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."}}