{"id":"W3171247947","doi":"10.1002/elan.202100224","title":"Accelerating Optimizing the Design of Carbon‐based Electrocatalyst via Machine Learning","year":2021,"lang":"en","type":"article","venue":"Electroanalysis","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"State Key Laboratory of Supramolecular Structure and Materials; Natural Science Foundation of Jilin Province","keywords":"Computer science; Throughput; Machine learning; Artificial intelligence; Electrocatalyst; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002688961,0.0003349501,0.0003595035,0.0002397991,0.0001660201,0.000359411,0.0004101655,0.0003989899,0.00102651],"category_scores_gemma":[0.0005063316,0.0001834194,0.0002090977,0.0003144841,0.0002446444,0.0004495286,0.0002300437,0.0005028479,0.0002577802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004068148,"about_ca_system_score_gemma":0.0005411609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005241538,"about_ca_topic_score_gemma":0.001003861,"domain_scores_codex":[0.9998834,0.00001791048,0.000005722601,0.00002889823,0.00004722546,0.00001688251],"domain_scores_gemma":[0.9998935,0.00004483239,0.00001538071,0.00001020974,0.00002915592,0.000006855752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001220679,0.0002273571,0.001120714,0.0005068398,0.00005976445,0.0001043391,0.00003543633,0.6149552,0.2383889,0.02503547,0.001746993,0.1176969],"study_design_scores_gemma":[0.000009376147,0.00005599424,0.000136952,0.000005851099,0.000006375449,0.00001387106,0.00000390279,0.9648384,0.03144747,0.001942629,0.001533988,0.000005135309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2108951,0.002080315,0.7727312,0.0004608596,0.0001194374,0.000120285,0.00008560347,0.0007921593,0.01271497],"genre_scores_gemma":[0.8158638,0.0007147688,0.1807849,0.0001031348,0.00001631094,0.0001030813,0.00009190908,0.00006820932,0.002253887],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00102651,"threshold_uncertainty_score":0.003434002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01688053506843964,"score_gpt":0.2441571095726279,"score_spread":0.2272765745041882,"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."}}