{"id":"W4407041620","doi":"10.1021/acs.langmuir.4c04638","title":"Automated Machine Learning of Interfacial Interaction Descriptors and Energies in Metal-Catalyzed N<sub>2</sub> and CO<sub>2</sub> Reduction Reactions","year":2025,"lang":"en","type":"article","venue":"Langmuir","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Catalysis; Chemistry; Electronegativity; Redox; Dimensionality reduction; Transition metal; Reactivity (psychology); Metal; Computer science; Inorganic chemistry; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009110959,0.0002276234,0.0003903135,0.0004716369,0.0002372021,0.0001556176,0.0001695852,0.0001203698,0.00003116797],"category_scores_gemma":[0.0004175867,0.000217409,0.00004053515,0.0004618479,0.0002934257,0.0005865369,0.0001799216,0.0002734948,0.00001950107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009156865,"about_ca_system_score_gemma":0.00004072371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000675077,"about_ca_topic_score_gemma":0.0001431132,"domain_scores_codex":[0.9980766,0.0003849702,0.0005347656,0.0004941794,0.0002171976,0.0002923527],"domain_scores_gemma":[0.999253,0.0001152238,0.0003085693,0.0001943441,0.00007321486,0.00005568409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001028237,0.00004513488,0.002413693,0.000126509,0.00001247993,0.000003255522,0.000816828,0.001408386,0.9932132,0.0002609539,0.00007797947,0.001518786],"study_design_scores_gemma":[0.0004164427,0.00005634362,0.01718956,0.0001537279,0.00003148371,0.00003779716,0.0003906921,0.01440916,0.9667581,0.0001134599,0.0002596857,0.0001835172],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975877,0.0001581178,0.0003437442,0.0001712826,0.0009238905,0.0001907195,0.00001837155,0.0003699933,0.000236205],"genre_scores_gemma":[0.9992484,0.00009570594,0.0004587963,0.00001817185,0.00004429765,0.00002989909,0.00003803153,0.00001914766,0.00004752981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02645504,"threshold_uncertainty_score":0.886568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01101455148060663,"score_gpt":0.267018330465013,"score_spread":0.2560037789844063,"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."}}