{"id":"W3092670396","doi":"10.1007/s00894-020-04484-4","title":"Reliability of semiempirical and DFTB methods for the global optimization of the structures of nanoclusters","year":2020,"lang":"en","type":"article","venue":"Journal of Molecular Modeling","topic":"Advanced Chemical Physics Studies","field":"Physics and Astronomy","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Fundação de Amparo à Pesquisa e Inovação do Espírito Santo; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Benchmark (surveying); Nanoclusters; Parametrization (atmospheric modeling); Yield (engineering); Computer science; Global optimization; Reliability (semiconductor); Set (abstract data type); Mathematical optimization; Algorithm; Mathematics; Nanotechnology; Materials science; Physics; Quantum mechanics","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":[0.003037721,0.0009534695,0.001502911,0.0008883025,0.0009312055,0.0009966376,0.001478623,0.00144386,0.003051425],"category_scores_gemma":[0.007026412,0.0004719565,0.0006275378,0.0009889402,0.00066535,0.0009169045,0.0008312581,0.001465677,0.0007178622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000973784,"about_ca_system_score_gemma":0.001524992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007132084,"about_ca_topic_score_gemma":0.006459272,"domain_scores_codex":[0.9989593,0.0006086337,0.00004984752,0.00006659238,0.0002583473,0.00005731159],"domain_scores_gemma":[0.994036,0.003863068,0.0001827137,0.0008601682,0.0009468675,0.000111182],"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":[0.0005384372,0.0002000488,0.001693109,0.0003377445,0.00009865654,0.00005534722,0.00009820335,0.9513713,0.002821111,0.008491419,0.002885863,0.03140874],"study_design_scores_gemma":[0.00003206473,0.00003733327,0.0002562759,0.00001352678,0.00000580761,0.000007518559,0.00001300357,0.9973369,0.00099685,0.001097665,0.0001958296,0.000007219696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7923633,0.004179313,0.1697797,0.001764494,0.0002751697,0.0001706356,0.002040569,0.003321572,0.02610528],"genre_scores_gemma":[0.9540279,0.000450443,0.0421199,0.0001570176,0.00004256864,0.0001887626,0.0009965169,0.0007047962,0.001312235],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.007132084,"threshold_uncertainty_score":0.01606518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02132396093038544,"score_gpt":0.331977512258521,"score_spread":0.3106535513281355,"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."}}