{"id":"W4385776964","doi":"10.1016/j.commatsci.2023.112432","title":"An accelerated strategy to characterize mechanical properties of polymer composites using the ensemble learning approach","year":2023,"lang":"en","type":"article","venue":"Computational Materials Science","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Artificial neural network; Mean squared error; Computer science; Polycarbonate; Artificial intelligence; Machine learning; Materials science; Characterization (materials science); Property (philosophy); Test data; Polyethylene terephthalate; Composite material; Mathematics; Statistics; Nanotechnology","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":[0.0006265611,0.0005391308,0.0004543668,0.000506798,0.0003000691,0.0004047914,0.0006481425,0.0005491753,0.001276827],"category_scores_gemma":[0.001369914,0.0002339432,0.0004991326,0.000384053,0.0002815803,0.001037778,0.0006183314,0.0008830106,0.0003253676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002246245,"about_ca_system_score_gemma":0.0004696652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001075192,"about_ca_topic_score_gemma":0.001680542,"domain_scores_codex":[0.9998135,0.00004107046,0.000006979672,0.00004288251,0.00006824399,0.00002724276],"domain_scores_gemma":[0.9994622,0.0001914897,0.00005462484,0.0001077249,0.0001517542,0.00003230673],"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":[0.0002033051,0.0003319862,0.003602822,0.00008386273,0.0001180133,0.0001091116,0.00006588314,0.6698847,0.1016361,0.01403569,0.001158376,0.2087701],"study_design_scores_gemma":[0.000001434249,0.00001550247,0.0002298212,6.315034e-7,0.000003065529,0.000007195154,0.000001852827,0.9952035,0.003518364,0.0009174947,0.00009852208,0.000002620186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1089306,0.00007684628,0.8888705,0.00007476514,0.00003147755,0.00003005563,0.00008192164,0.0004525116,0.00145133],"genre_scores_gemma":[0.7169789,0.0001045654,0.2798342,0.00005847911,0.00003572905,0.0001256608,0.0002925364,0.0001214295,0.002448476],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001276827,"threshold_uncertainty_score":0.004271388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08403842183990887,"score_gpt":0.3181135013394341,"score_spread":0.2340750794995252,"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."}}