{"id":"W4310251330","doi":"10.1002/jcc.27043","title":"<scp>GAMaterial</scp>—A genetic‐algorithm software for material design and discovery","year":2022,"lang":"en","type":"article","venue":"Journal of Computational Chemistry","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Calgary","funders":"Fundação de Amparo à Pesquisa e Inovação do Espírito Santo; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Cluster (spacecraft); Dopant; Software; Genetic algorithm; Computer science; Graphene; Algorithm; Computational science; Doping; Nanotechnology; Materials science; Biological system; Machine learning","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":[0.001308036,0.0001740966,0.0003035797,0.00005257835,0.0003754062,0.0003821998,0.000477087,0.000047479,0.0004275078],"category_scores_gemma":[0.0005042016,0.000164967,0.00006802995,0.0001035192,0.0001218691,0.0003070309,0.0002437856,0.0001435469,0.000003323068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009224195,"about_ca_system_score_gemma":0.0003159351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002772188,"about_ca_topic_score_gemma":1.380051e-8,"domain_scores_codex":[0.9980238,0.0001742501,0.0006006309,0.0002616904,0.0006722935,0.0002673497],"domain_scores_gemma":[0.9981272,0.0006847135,0.0007025977,0.0001142526,0.0002596453,0.0001116536],"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":[0.00006737308,0.00004599067,0.00004962565,0.00007242755,0.00001000504,0.00002126159,0.0001202911,0.4344013,0.5630698,0.000009693304,0.001743737,0.0003885891],"study_design_scores_gemma":[0.002463106,0.0007678504,0.002795762,0.00007179356,0.00009367087,0.003597212,0.0003174239,0.03034513,0.9046401,0.04324941,0.01130228,0.000356278],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6668736,0.00006532562,0.3317086,0.00006450069,0.0009558646,0.0001146614,0.0001816551,0.0000227062,0.00001304617],"genre_scores_gemma":[0.2418791,0.000005440353,0.7565044,0.0001387033,0.0009927265,0.00004168179,0.00004128207,0.00003686114,0.0003598038],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4249945,"threshold_uncertainty_score":0.6727157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009637391813891812,"score_gpt":0.2440704195053743,"score_spread":0.2344330276914825,"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."}}