{"id":"W2313809507","doi":"10.1021/ct2007742","title":"Automated Parametrization of AMBER Force Field Terms from Vibrational Analysis with a Focus on Functionalizing Dinuclear Zinc(II) Scaffolds","year":2012,"lang":"en","type":"article","venue":"Journal of Chemical Theory and Computation","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"College of Family Physicians of Canada; University of Toronto; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; McMaster University","keywords":"Parametrization (atmospheric modeling); Focus (optics); Force field (fiction); Zinc; Field (mathematics); Computer science; Computational chemistry; Nanotechnology; Physics; Chemistry; Materials science; Quantum mechanics; Artificial intelligence; Mathematics; Pure mathematics; Optics; Organic chemistry","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":[0.0006250267,0.001249802,0.0007256707,0.0006426399,0.0005939991,0.0005556631,0.00144378,0.0005777822,0.01195647],"category_scores_gemma":[0.001669184,0.0003914564,0.000437544,0.000569997,0.0001678487,0.0005204094,0.0005734368,0.001227361,0.002205986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000497825,"about_ca_system_score_gemma":0.0008043062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00274488,"about_ca_topic_score_gemma":0.004039211,"domain_scores_codex":[0.9997993,0.00004836834,0.00001301302,0.0000248082,0.00008937892,0.00002518739],"domain_scores_gemma":[0.9996901,0.0001205624,0.00002657654,0.00007444327,0.0000752073,0.00001314435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004178824,0.0003042421,0.002060108,0.0006098099,0.0001204367,0.0003957973,0.0003673453,0.4361328,0.1164467,0.04720161,0.02023266,0.3757107],"study_design_scores_gemma":[0.00006662233,0.00005847806,0.0006518239,0.00003748854,0.00001666418,0.00008057144,0.00003919065,0.9460803,0.02686056,0.007434268,0.01862112,0.00005296269],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07058023,0.0003093853,0.9016733,0.0002064925,0.000112477,0.0002681734,0.002104127,0.01698517,0.007760683],"genre_scores_gemma":[0.219042,0.0003615962,0.7658771,0.0001067336,0.00003181459,0.001031508,0.003308709,0.004985663,0.005254843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01195647,"threshold_uncertainty_score":0.03999829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009333500194298325,"score_gpt":0.272726212291111,"score_spread":0.2633927120968127,"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."}}