{"id":"W2761605604","doi":"10.1021/acs.jctc.7b00380","title":"Accurate Prediction of NMR Chemical Shifts in Macromolecular and Condensed-Phase Systems with the Generalized Energy-Based Fragmentation Method","year":2017,"lang":"en","type":"article","venue":"Journal of Chemical Theory and Computation","topic":"Advanced Chemical Physics Studies","field":"Physics and Astronomy","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"Ministry of Education of the People's Republic of China; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Polarizable continuum model; Electromagnetic shielding; Chemical shift; Density functional theory; Chemistry; Molecular dynamics; Quantum chemistry; Solvent models; Computational chemistry; Nuclear magnetic resonance spectroscopy; Statistical physics; Solvent effects; Molecule; Molecular physics; Physical chemistry; Supramolecular chemistry; Physics; Quantum mechanics; Solvent; Solvation; Organic chemistry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005844229,0.0006202071,0.0005504975,0.0004584368,0.0003761128,0.0002959715,0.0008234265,0.0007559764,0.001341966],"category_scores_gemma":[0.0009415788,0.0002907047,0.0005072189,0.0003569744,0.000445263,0.0005824929,0.0004194809,0.0005766343,0.0002570706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007052812,"about_ca_system_score_gemma":0.0009756632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002668627,"about_ca_topic_score_gemma":0.00209399,"domain_scores_codex":[0.9998789,0.00003543757,0.000004781558,0.00001370185,0.00004961318,0.000017551],"domain_scores_gemma":[0.9997724,0.000101082,0.00002527114,0.00003439065,0.00004740359,0.00001950448],"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":[0.00005353142,0.00003328403,0.000600555,0.0001291408,0.00004914746,0.00019456,0.00008268494,0.9190017,0.03230412,0.03099358,0.0008237232,0.01573395],"study_design_scores_gemma":[0.000005738965,0.000007399314,0.00009849619,0.000002041262,0.000001747663,0.00001167384,0.000003243075,0.9955049,0.00124234,0.002899353,0.0002187212,0.000004339809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1134947,0.00044662,0.8806801,0.000146912,0.00004314795,0.00006514034,0.0001927309,0.0006404634,0.004290191],"genre_scores_gemma":[0.650557,0.0004290098,0.3448941,0.0001252506,0.00003401405,0.0003015444,0.0004750683,0.0003094845,0.002874439],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002668627,"threshold_uncertainty_score":0.005306184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01321714106098192,"score_gpt":0.3081053037036139,"score_spread":0.294888162642632,"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."}}