{"id":"W4214970126","doi":"10.1021/ct501032v.s001","title":"Probing potential energy surface exploration strategies for complex\\n systems","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Ion-surface interactions and analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Islamic Development Bank; Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Statistical physics; Kinetic Monte Carlo; Kinetic energy; Energy landscape; Lattice (music); Complex system; Relaxation (psychology); Monte Carlo method; Energy (signal processing); Tabu search; Potential energy surface; Physics; Computer science; Algorithm; Mathematics; Quantum mechanics; Thermodynamics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001029049,0.0002780611,0.0003459738,0.0001582046,0.0001514304,0.0002414127,0.0002709527,0.0002200427,0.00002637495],"category_scores_gemma":[0.000003612416,0.0003330134,0.0002404595,0.0001778538,0.00003030636,0.0004037585,0.0001038897,0.0002204005,0.00001964979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001890886,"about_ca_system_score_gemma":0.00004833638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004166391,"about_ca_topic_score_gemma":0.0001091287,"domain_scores_codex":[0.9989887,0.00005275482,0.0002278602,0.0004259109,0.0000564574,0.0002483265],"domain_scores_gemma":[0.9992014,0.00004529061,0.0001300307,0.0003751217,0.0001804292,0.00006770925],"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":[0.00001213491,0.00001599376,0.00001322153,0.0002156561,0.0002235121,0.000008405152,0.00006066428,0.9751261,0.001741631,0.02176945,0.0007773419,0.00003584853],"study_design_scores_gemma":[0.0001896824,0.00001820391,0.000006574588,0.00009775121,0.000179768,0.000001444463,0.001049863,0.9919009,0.000164575,0.003574537,0.002470935,0.0003457175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1886295,0.00005228418,0.808665,0.00001367664,0.0008899987,0.0001759484,0.00004354366,0.000296734,0.001233342],"genre_scores_gemma":[0.9980857,0.0001193207,0.0003566708,0.000005146377,0.0001821402,0.0000038484,0.0002377448,0.00004474608,0.0009646952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8094562,"threshold_uncertainty_score":0.9999122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06663442644942186,"score_gpt":0.1831379480484699,"score_spread":0.116503521599048,"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."}}