{"id":"W4381742992","doi":"10.1021/acscatal.3c01249","title":"Combined High-Throughput DFT and ML Screening of Transition Metal Nitrides for Electrochemical CO<sub>2</sub> Reduction","year":2023,"lang":"en","type":"article","venue":"ACS Catalysis","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Research Foundation of Korea; National Research Foundation; National Research Council Canada; Korea Institute of Science and Technology Information; Compute Canada","keywords":"Density functional theory; Catalysis; Transition metal; Materials science; Throughput; Electrochemistry; Chemical stability; Renewable energy; Stability (learning theory); Nanotechnology; Chemistry; Computational chemistry; Computer science; Physical chemistry; Machine learning; Organic chemistry; Electrode","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":[0.0002700795,0.0001815151,0.0003777903,0.0002977049,0.000139999,0.00002469433,0.0001082078,0.0001464892,0.00001073743],"category_scores_gemma":[0.00005831482,0.0001915957,0.0002045773,0.0008460952,0.0001077827,0.0002235245,0.00003091177,0.000112686,0.000007629755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004212094,"about_ca_system_score_gemma":0.00002149232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002989831,"about_ca_topic_score_gemma":0.00001946927,"domain_scores_codex":[0.9986986,0.00003320707,0.0003806594,0.0003890654,0.0002281508,0.0002703099],"domain_scores_gemma":[0.9992904,0.00006954397,0.0001447923,0.0002887333,0.0001379682,0.00006852865],"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":[0.0001566475,0.00004728207,0.000005981392,0.00005430997,0.0002348151,0.000001082959,0.0002305656,0.00007821294,0.9725015,0.0009890618,0.0009174122,0.0247831],"study_design_scores_gemma":[0.0005560865,0.0001418913,0.0001067931,0.00001832646,0.0003871107,0.00001918301,0.0003123895,0.0003898955,0.9946212,0.002832075,0.0004202734,0.0001948005],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894432,0.000109285,0.009272386,0.0004657774,0.0000648631,0.0002255681,0.00003627671,0.0003340406,0.00004861827],"genre_scores_gemma":[0.9959128,0.0002761091,0.000743432,0.00002022547,0.0001456061,0.0001341054,0.002651742,0.00003820939,0.00007779399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0245883,"threshold_uncertainty_score":0.7813044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01255328791797792,"score_gpt":0.2463515474153109,"score_spread":0.233798259497333,"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."}}