{"id":"W4403762775","doi":"10.1016/j.matt.2024.10.001","title":"High-entropy alloy electrocatalysts screened using machine learning informed by quantum-inspired similarity analysis","year":2024,"lang":"en","type":"article","venue":"Matter","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vector Institute; University of Ottawa; Canada Research Chairs; University of Toronto","funders":"Fujitsu; Government of Ontario; University of Toronto; Canada Foundation for Innovation; Ontario Research Foundation","keywords":"Alloy; Artificial intelligence; Similarity (geometry); Quantum; Entropy (arrow of time); Computer science; Machine learning; Materials science; Physics; Metallurgy; Quantum mechanics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002880152,0.0002337361,0.0007110139,0.0005494708,0.0003860989,0.0008007243,0.0006619607,0.0006399518,0.001782757],"category_scores_gemma":[0.0006559882,0.0002276438,0.0003327448,0.0004652477,0.0003923758,0.0007820764,0.0005135341,0.0005066046,0.0003234535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004087035,"about_ca_system_score_gemma":0.0003766291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001106474,"about_ca_topic_score_gemma":0.002898234,"domain_scores_codex":[0.999846,0.00002173756,0.000005358757,0.00002109671,0.0000807027,0.00002511023],"domain_scores_gemma":[0.9998115,0.00009122366,0.00001505999,0.00003083113,0.00003355803,0.00001782444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001030485,0.0003524814,0.002962543,0.0005927036,0.0001840164,0.0007154782,0.0001799886,0.2322243,0.6205217,0.09147468,0.003254059,0.04650756],"study_design_scores_gemma":[0.00002620763,0.00004744421,0.0005322223,0.000004430478,0.00001591314,0.00003043703,0.00001962812,0.9458185,0.04823488,0.004755397,0.0005016647,0.00001321946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8891196,0.0006862852,0.09680892,0.0003508307,0.0001722469,0.0000622193,0.0001792303,0.001096557,0.01152405],"genre_scores_gemma":[0.990797,0.00009439501,0.007750337,0.00004513819,0.00001090849,0.00001468881,0.00007324269,0.0000385398,0.00117566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001782757,"threshold_uncertainty_score":0.005963862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0107454961366416,"score_gpt":0.2648442520183199,"score_spread":0.2540987558816783,"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."}}