{"id":"W4231098174","doi":"10.32920/ryerson.14656692","title":"Variable time domain discretization methodology for molecular dynamics simulation of metallic compounds","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Copper Interconnects and Reliability","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Molecular dynamics; Discretization; Work (physics); Computer science; Computation; Variable (mathematics); Time domain; Kinetic energy; Statistical physics; Simulation; Computational chemistry; Mechanical engineering; Chemistry; Algorithm; Physics; Mathematics; Classical mechanics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004029417,0.0003417044,0.0004748672,0.0003003299,0.000384516,0.0005086423,0.001156976,0.0007748747,0.002643837],"category_scores_gemma":[0.0008305649,0.0002371721,0.0006158929,0.0004675123,0.0003778128,0.0004092981,0.0004592831,0.00105874,0.0005877908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005239315,"about_ca_system_score_gemma":0.00102959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003420244,"about_ca_topic_score_gemma":0.002443403,"domain_scores_codex":[0.9998079,0.00006451711,0.000009494101,0.00001999379,0.0000773468,0.00002072235],"domain_scores_gemma":[0.999775,0.00009929258,0.00002420152,0.00003409318,0.00005248309,0.00001500893],"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":[0.00006424086,0.00006792737,0.001303045,0.0002285953,0.00005701763,0.0001316463,0.0001235753,0.8622867,0.01645026,0.07624235,0.001792787,0.0412518],"study_design_scores_gemma":[0.00001300127,0.00001955965,0.0001159988,0.00001031707,0.000004110193,0.00002041063,0.000008778871,0.9908649,0.001635906,0.002638953,0.004662142,0.000005822791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.015664,0.0004185105,0.9766257,0.0001604036,0.0001403535,0.0001106413,0.0002317207,0.0004436887,0.006205042],"genre_scores_gemma":[0.2613957,0.0009181924,0.7304754,0.0001439295,0.00005407505,0.0009125888,0.0005094489,0.0002809787,0.005309701],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003420244,"threshold_uncertainty_score":0.008844495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03537261406876413,"score_gpt":0.3213320808711047,"score_spread":0.2859594668023405,"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."}}