{"id":"W4411544669","doi":"10.1016/j.jnoncrysol.2025.123682","title":"Molecular dynamics simulation and machine learning to predict mechanical behavior of Cu/Zr multilayer nanofilms under tension-compression","year":2025,"lang":"en","type":"article","venue":"Journal of Non-Crystalline Solids","topic":"Microstructure and mechanical properties","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"National Science and Technology Council","keywords":"Molecular dynamics; Tension (geology); Compression (physics); Materials science; Dynamics (music); Mechanical compression; Composite material; Biological system; Computer science; Chemistry; Physics; Engineering; Biomedical engineering; Computational chemistry; Acoustics","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":[],"consensus_categories":[],"category_scores_codex":[0.0005358552,0.0001824014,0.0004593867,0.0001769967,0.000100988,0.00004337556,0.0002187094,0.000150797,0.00006083696],"category_scores_gemma":[0.0002219711,0.0001291446,0.0001152742,0.0001553386,0.00004873891,0.0001564087,0.0002204637,0.0003074603,0.000001346799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000722012,"about_ca_system_score_gemma":0.0001030039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003146841,"about_ca_topic_score_gemma":0.000009811211,"domain_scores_codex":[0.9983181,0.0001117484,0.000768805,0.0002197093,0.0003659622,0.0002157069],"domain_scores_gemma":[0.9988418,0.00009898539,0.0003529306,0.0001817668,0.000383552,0.0001410141],"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":[0.0003276426,0.00007628158,0.00008793642,0.00005063259,0.00001638728,0.00001856269,0.00009591356,0.08079197,0.917311,0.00008352994,0.00002477989,0.001115356],"study_design_scores_gemma":[0.001067247,0.0006209851,0.0005394025,0.0004251558,0.0001458584,0.00004293425,0.000133709,0.3982097,0.597877,0.0004909368,0.0002831274,0.0001640003],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8047874,0.0002187666,0.1942833,0.0001989136,0.0002722177,0.0001922027,0.00001236004,0.00001310307,0.00002171353],"genre_scores_gemma":[0.9905276,0.00002873045,0.008971732,0.0002441432,0.00006037316,0.000002317542,0.000005449788,0.00001960459,0.0001401058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3194341,"threshold_uncertainty_score":0.5266365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436692834652158,"score_gpt":0.2853073644342676,"score_spread":0.270940436087746,"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."}}