{"id":"W4415445396","doi":"10.38094/jastt62459","title":"Physics-Informed Machine Learning Framework for Virtual Screening and Multi-Objective Optimization of Polymer Nanocomposites with Tailored Multifunctional Properties","year":2025,"lang":"","type":"article","venue":"Journal of Applied Science and Technology Trends","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Department of Artificial Intelligence, Korea University","keywords":"Artificial neural network; Simulated annealing; Nanocomposite; Polymer; Polymer nanocomposite; Deep learning; Optimization problem; Thermal","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001921087,0.0003636376,0.0007378121,0.001699812,0.001258311,0.0003286214,0.0007194603,0.0002686614,0.00003193951],"category_scores_gemma":[0.0009464725,0.0002661811,0.00005443241,0.002947741,0.00547746,0.0008464983,0.0004165619,0.0006077178,4.151363e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000973154,"about_ca_system_score_gemma":0.0007517041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001583476,"about_ca_topic_score_gemma":0.000003676821,"domain_scores_codex":[0.9971365,0.00005093519,0.0008866137,0.0006326276,0.0007448141,0.0005485453],"domain_scores_gemma":[0.9967178,0.0003313723,0.001503412,0.0002377568,0.00109295,0.0001166851],"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":[0.00179953,0.0001664104,0.003990523,0.0001285995,0.00008144718,0.000001945901,0.001348273,0.09039271,0.8333974,0.01592801,0.000003823372,0.05276128],"study_design_scores_gemma":[0.00254601,0.001827454,0.001004291,0.0006942812,0.0001642243,0.00006112803,0.002414618,0.3922063,0.597986,0.0007173857,0.00005203264,0.0003262206],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4390226,0.0007197434,0.558432,0.0008975887,0.000409886,0.0003111861,0.0000136927,0.00005375994,0.0001395667],"genre_scores_gemma":[0.7595946,0.00007721662,0.2400791,0.00004774117,0.00004509855,0.00002227341,0.000001435218,0.00001387797,0.0001186503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.320572,"threshold_uncertainty_score":0.999979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01195810117505891,"score_gpt":0.2603428067377461,"score_spread":0.2483847055626872,"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."}}