{"id":"W3060694946","doi":"10.1063/5.0015039","title":"Accelerated kinetic Monte Carlo: A case study; vacancy and dumbbell interstitial diffusion traps in concentrated solid solution alloys","year":2020,"lang":"en","type":"article","venue":"The Journal of Chemical Physics","topic":"High Entropy Alloys Studies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Basic Energy Sciences; Natural Sciences and Engineering Research Council of Canada; Office of Science; Compute Canada; University Network of Excellence in Nuclear Engineering; U.S. Department of Energy","keywords":"Dumbbell; Kinetic Monte Carlo; Diffusion; Vacancy defect; Lattice diffusion coefficient; Effective diffusion coefficient; Thermodynamics; Materials science; Chemistry; Monte Carlo method; Crystallography; Physics","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":[0.0005113137,0.0003794844,0.0005616375,0.0004142421,0.0004588416,0.0006856099,0.0009626265,0.001162197,0.001094252],"category_scores_gemma":[0.001260781,0.0002637554,0.0004672503,0.0005778224,0.0005888888,0.0004414374,0.0004262197,0.000540534,0.00009528617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007806214,"about_ca_system_score_gemma":0.0006564686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00934687,"about_ca_topic_score_gemma":0.005654927,"domain_scores_codex":[0.9998422,0.0000504575,0.000005857418,0.00001826674,0.00005631775,0.00002689899],"domain_scores_gemma":[0.9991919,0.0005062544,0.00007010267,0.0000493391,0.0001353105,0.00004699725],"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":[0.00007068359,0.00006759319,0.001681251,0.0001044095,0.00002367096,0.0004368124,0.0000758508,0.9643298,0.004301216,0.02523243,0.000362031,0.003314182],"study_design_scores_gemma":[0.00001309772,0.00002432451,0.0001483611,0.000005139008,0.000006328278,0.00005046009,0.00001296322,0.9958254,0.00137122,0.002090507,0.0004474718,0.000004713665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8978946,0.00221696,0.08819044,0.0004876998,0.00006532302,0.00008947726,0.000278084,0.0002927145,0.0104847],"genre_scores_gemma":[0.9757015,0.0003253805,0.02209426,0.0000293399,0.00001229502,0.0000462636,0.000070265,0.00002828906,0.001692366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00934687,"threshold_uncertainty_score":0.01858491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0241605070854774,"score_gpt":0.2448250355743877,"score_spread":0.2206645284889103,"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."}}