{"id":"W4405055249","doi":"10.1021/acscatal.4c06026","title":"Automated Exploration of Heterogeneous Catalysis with a Gas–Solid Nanoreactor","year":2024,"lang":"en","type":"article","venue":"ACS Catalysis","topic":"Catalytic Processes in Materials Science","field":"Materials Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; Youth Innovation Promotion Association of the Chinese Academy of Sciences; National Science Fund for Distinguished Young Scholars; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Nanoreactor; Catalysis; Heterogeneous catalysis; Chemical engineering; Chemistry; Materials science; Nanotechnology; Organic chemistry; Engineering","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":[0.0006219118,0.0003047364,0.0005239933,0.0003566646,0.0001261275,0.0003816175,0.0007449985,0.00009732141,0.0003355622],"category_scores_gemma":[0.0001802543,0.0002340312,0.0001193229,0.00165008,0.0003783666,0.001641621,0.0002146348,0.00007827199,0.0005884753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001304908,"about_ca_system_score_gemma":0.0002511768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003343804,"about_ca_topic_score_gemma":0.0001435666,"domain_scores_codex":[0.997299,0.00005488717,0.0006257249,0.0008025745,0.0007758249,0.000442041],"domain_scores_gemma":[0.9983016,0.000139299,0.0002456505,0.0009351156,0.0002468571,0.0001314911],"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":[0.00003984095,0.00007472963,0.00002520105,0.0002396144,0.00007797445,0.00007203406,0.001292581,0.001436376,0.9956232,0.00009262437,0.0002469227,0.0007789002],"study_design_scores_gemma":[0.0001242675,0.000111736,0.00001910063,0.0001252557,0.0003036674,0.0001078347,0.0001572985,0.005807491,0.9923673,0.0002812754,0.0002957656,0.0002989516],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938604,0.0004820648,0.003505337,0.0001994249,0.0004402834,0.0002156577,0.0001309329,0.00095621,0.0002096549],"genre_scores_gemma":[0.9977813,0.00005181588,0.001516445,0.00004079237,0.0000838081,0.00009346689,0.0002513352,0.00004599426,0.0001350373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004371115,"threshold_uncertainty_score":0.9543517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01541036442013186,"score_gpt":0.2724119616370126,"score_spread":0.2570015972168807,"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."}}