{"id":"W4321085451","doi":"10.1021/acscatal.2c05426","title":"The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts","year":2023,"lang":"en","type":"article","venue":"ACS Catalysis","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":288,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; The Scarborough Hospital; University of Toronto","funders":"","keywords":"Benchmark (surveying); Baseline (sea); Computer science; Scaling; Range (aeronautics); Oxide; Set (abstract data type); Machine learning; Materials science; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001235781,0.002509397,0.001105028,0.001743151,0.001260932,0.001745592,0.0044228,0.003596986,0.005867952],"category_scores_gemma":[0.004017999,0.0004739568,0.001978973,0.001958449,0.0006957531,0.002144769,0.001650078,0.002414197,0.005242869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002297956,"about_ca_system_score_gemma":0.001546059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03446447,"about_ca_topic_score_gemma":0.05952573,"domain_scores_codex":[0.9989028,0.0001629409,0.0000622307,0.0003575051,0.0003835976,0.0001309069],"domain_scores_gemma":[0.9990069,0.0002572609,0.00005731738,0.000282639,0.000280454,0.0001154348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005965496,0.001009873,0.01085997,0.001775904,0.0002781878,0.000437394,0.00009187598,0.07197861,0.004751759,0.006428181,0.8534971,0.04829453],"study_design_scores_gemma":[0.0008962795,0.0005063771,0.01705495,0.0004155901,0.0001625836,0.0006000242,0.0004509951,0.4367729,0.02250797,0.01828384,0.5021518,0.0001966905],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1840299,0.005981866,0.01007137,0.006641652,0.00143982,0.0004443665,0.7405509,0.01772611,0.033114],"genre_scores_gemma":[0.06327125,0.0006304288,0.01177915,0.0007739583,0.0001019606,0.000263094,0.9170443,0.0006604308,0.005475469],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03446447,"threshold_uncertainty_score":0.06852776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0375950222084821,"score_gpt":0.3100737398999703,"score_spread":0.2724787176914882,"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."}}