{"id":"W4393407738","doi":"10.1039/d3dd00244f","title":"A multiobjective closed-loop approach towards autonomous discovery of electrocatalysts for nitrogen reduction","year":2024,"lang":"en","type":"article","venue":"Digital Discovery","topic":"Ammonia Synthesis and Nitrogen Reduction","field":"Chemical Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Advanced Research Projects Agency; Advanced Research Projects Agency - Energy; U.S. Department of Energy","keywords":"Reduction (mathematics); Closed loop; Loop (graph theory); Scheme (mathematics); Nitrogen; Nitrogen atom; Computer science; Multi-objective optimization; Chemistry; Engineering; Mathematics; Control engineering; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009961942,0.0002866868,0.0003650572,0.0001715482,0.00005279064,0.0003736235,0.0001641262,0.0001388344,0.000004698383],"category_scores_gemma":[0.000103188,0.0002540424,0.0004823559,0.0003209611,0.00008844693,0.002544517,0.00005826348,0.000168906,0.00001309525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001993172,"about_ca_system_score_gemma":0.0001561618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003711585,"about_ca_topic_score_gemma":0.0000015538,"domain_scores_codex":[0.9984237,0.000011961,0.0004143281,0.0005374444,0.0002416623,0.0003709377],"domain_scores_gemma":[0.9994475,0.00007712081,0.00008765145,0.0002628096,0.00006235566,0.00006257571],"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":[0.0004651579,0.0006107752,0.00006856759,0.001034685,0.001089187,0.000006117553,0.0006640384,0.001231973,0.9181762,0.01869048,0.0006221731,0.05734063],"study_design_scores_gemma":[0.0003349571,0.0001411879,0.00007088146,0.0001271953,0.0001631533,0.00007212131,0.0005905814,0.02039246,0.974355,0.002510859,0.0007969579,0.0004445889],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8452372,0.001629137,0.1465312,0.00004388809,0.0004536582,0.0006352172,0.0008837768,0.0003059919,0.004279961],"genre_scores_gemma":[0.9956393,0.00001700078,0.0006323651,0.00000427272,0.0004034659,0.0002042255,0.0005827756,0.00008084564,0.002435717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1504021,"threshold_uncertainty_score":0.9999912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01198000577169909,"score_gpt":0.2364721866679845,"score_spread":0.2244921808962854,"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."}}