{"id":"W4413338830","doi":"10.1063/5.0280842","title":"Prediction of vacancy defect diffusion paths in high entropy alloys via machine learning on molecular dynamics data","year":2025,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"High Entropy Alloys Studies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of Waterloo; National Research Council Canada; Canadian Nuclear Laboratories; Vector Institute; University of Ottawa","funders":"Basic Energy Sciences; Office of Science; Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Atomic Energy of Canada Limited; U.S. Department of Energy; Canada Excellence Research Chairs, Government of Canada; Belgian American Educational Foundation","keywords":"Vacancy defect; Statistical physics; Molecular dynamics; High entropy alloys; Materials science; Diffusion; Entropy (arrow of time); Chemical physics; Thermodynamics; Condensed matter physics; Physics; Chemistry; Computational chemistry; Metallurgy; Microstructure","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":[0.0004645683,0.0005131564,0.0003167252,0.0006368592,0.0002789565,0.0004610351,0.0005178828,0.0007269747,0.0005866163],"category_scores_gemma":[0.002189159,0.0003468683,0.0003827722,0.0003222586,0.0003889886,0.0006364841,0.0002703515,0.0008459972,0.0001308889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001063844,"about_ca_system_score_gemma":0.000771759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01227421,"about_ca_topic_score_gemma":0.009830965,"domain_scores_codex":[0.9999216,0.00002048314,0.00000477028,0.00002537255,0.00001573264,0.00001206926],"domain_scores_gemma":[0.9993821,0.0003726642,0.00007791787,0.00004423284,0.0000800121,0.00004291481],"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":[0.00002749873,0.00002251768,0.00222067,0.00001703226,0.000009394487,0.00002659055,0.00001157282,0.9923282,0.001146573,0.0007958625,0.0001261777,0.003267854],"study_design_scores_gemma":[6.523962e-7,0.000002119183,0.0001122845,6.104406e-7,4.042053e-7,0.000001021781,8.480424e-7,0.9995186,0.0001884488,0.0001545864,0.00001962373,7.108652e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8956242,0.0003730158,0.100771,0.0004367613,0.00003488121,0.00005999383,0.0005949364,0.0007835987,0.001321584],"genre_scores_gemma":[0.9785084,0.0001303261,0.02036961,0.00002612765,0.000006068442,0.00003841773,0.0005009269,0.00003057923,0.0003895262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01227421,"threshold_uncertainty_score":0.02440554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006857986400854069,"score_gpt":0.2053568021842322,"score_spread":0.1984988157833781,"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."}}