{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004191178,0.00007579021,0.0001070087,0.00002487026,0.00011863,0.0000320589,0.00009300357,0.00004370939,0.0008114183],"category_scores_gemma":[0.000523327,0.00006725315,0.00001997944,0.0001442742,0.0004269689,0.00006163574,0.00007466533,0.00006853422,0.00002650971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001046749,"about_ca_system_score_gemma":0.00002751931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000986482,"about_ca_topic_score_gemma":4.597522e-8,"domain_scores_codex":[0.9992253,0.00005661094,0.0001745235,0.0001905089,0.0002294831,0.000123592],"domain_scores_gemma":[0.9994762,0.0002542581,0.0000787586,0.00006278048,0.00004657399,0.00008146388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000272623,0.000007417011,0.0002385039,0.00006740701,0.000002352434,0.00000239604,0.0000791233,0.1838354,0.5840139,0.2307068,0.00001953845,0.0009999059],"study_design_scores_gemma":[0.0001121227,0.00001657054,0.0005506822,0.00001709604,0.00000447939,0.00001397069,0.00002223011,0.7411991,0.1008421,0.1570589,0.00008802005,0.00007465481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9781207,0.00002275895,0.01931937,0.0002297857,0.00007441997,0.00002049965,0.00001137712,0.0001293913,0.002071649],"genre_scores_gemma":[0.9973649,0.000006343319,0.002371713,0.00002469742,0.00003327809,0.000004164121,0.00006731134,0.000006786748,0.0001207539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5573637,"threshold_uncertainty_score":0.8884462,"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."}}