{"id":"W4385827196","doi":"10.1021/acs.jctc.3c00566","title":"QMLMaterial─A Quantum Machine Learning Software for Material Design and Discovery","year":2023,"lang":"en","type":"article","venue":"Journal of Chemical Theory and Computation","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Calgary","funders":"Fundação Amazônia Paraense de Amparo à Pesquisa; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Artificial neural network; Machine learning; Software; Artificial intelligence; Curse of dimensionality; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003480802,0.0001219176,0.0002597329,0.00008224767,0.0001355404,0.0003249809,0.0001337758,0.00005965607,0.00003948583],"category_scores_gemma":[0.00113062,0.00009458125,0.00003369393,0.0001075988,0.0001342567,0.0004230335,0.00008840773,0.00009462115,0.000005536119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001491187,"about_ca_system_score_gemma":0.00003038837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001544423,"about_ca_topic_score_gemma":1.855673e-8,"domain_scores_codex":[0.9986243,0.0004195558,0.0003930634,0.0001790952,0.0001938795,0.0001900635],"domain_scores_gemma":[0.99839,0.00103671,0.0003503464,0.00004878114,0.00010165,0.00007251109],"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":[0.001079455,0.00001265453,0.00005514442,0.00008386253,0.000005819246,0.000005784618,0.0002478483,0.007217956,0.987615,0.00235829,0.00006833945,0.001249859],"study_design_scores_gemma":[0.001049738,0.0005198192,0.0003194072,0.0001329502,0.00004694619,0.0002158009,0.00009754376,0.05429253,0.7612606,0.1816158,0.0001931049,0.0002557541],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7079943,0.00003475568,0.2912289,0.0000840716,0.0005124095,0.00008962079,0.000009216062,0.00004463726,0.00000211591],"genre_scores_gemma":[0.9748335,0.00002108306,0.02478049,0.00004311237,0.0002559329,0.000005459952,0.00001308556,0.00001622707,0.00003105446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2668393,"threshold_uncertainty_score":0.3856911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0179183896593932,"score_gpt":0.2826650020623205,"score_spread":0.2647466124029273,"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."}}