{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005918432,0.0002863398,0.0003377226,0.0006889316,0.0004235798,0.0007782131,0.0007017306,0.0007876528,0.002170723],"category_scores_gemma":[0.003124731,0.0002552971,0.0002664608,0.0004298288,0.0007597755,0.001139605,0.0007765012,0.0005192113,0.0002169191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006897706,"about_ca_system_score_gemma":0.0005133384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001569061,"about_ca_topic_score_gemma":0.002031408,"domain_scores_codex":[0.9998745,0.00005375174,0.000005245406,0.00001781783,0.00002913693,0.00001967296],"domain_scores_gemma":[0.9993122,0.0004906473,0.00005683795,0.00006212827,0.00003688435,0.00004133149],"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":[0.0001093906,0.0001524634,0.004272528,0.0001366427,0.00003529945,0.0001049028,0.0002556406,0.8912567,0.006696972,0.06949542,0.0008205184,0.02666345],"study_design_scores_gemma":[0.000004303278,0.0000142195,0.0001887026,0.000003767005,0.00000124261,0.000007976911,0.0000281926,0.9890955,0.000438341,0.01003208,0.0001825876,0.000003012843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7697338,0.0006752504,0.2168865,0.0006580796,0.00002581131,0.00007619001,0.0001381585,0.0003042934,0.01150197],"genre_scores_gemma":[0.9488432,0.000218839,0.04967596,0.00003999328,0.000004917097,0.00006973694,0.00009495219,0.00004989177,0.001002539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002170723,"threshold_uncertainty_score":0.007261753,"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."}}