{"id":"W2780446390","doi":"10.1021/acs.jpcc.7b12314","title":"Refining Crystal Structures with Quadrupolar NMR and Dispersion-Corrected Density Functional Theory","year":2017,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry C","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Genentech","keywords":"Neutron diffraction; Tensor (intrinsic definition); Density functional theory; Parametrization (atmospheric modeling); Electric field gradient; Diffraction; Dispersion (optics); Crystal (programming language); Crystal structure; Materials science; Chemistry; Molecular physics; Crystallography; Computational chemistry; Physics; Electric field; Mathematics; Geometry; Computer science; Optics; Quantum mechanics","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":[0.001081772,0.0008314411,0.0006361054,0.0007162201,0.0007569506,0.0008138485,0.00117477,0.0006292651,0.001434267],"category_scores_gemma":[0.001753019,0.0004699644,0.0005858199,0.0008059058,0.0004878629,0.0008901122,0.000590222,0.001389963,0.0004280691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001278623,"about_ca_system_score_gemma":0.002205411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007401206,"about_ca_topic_score_gemma":0.01314563,"domain_scores_codex":[0.9996679,0.00006555874,0.00001764444,0.00004627782,0.0001732634,0.00002933079],"domain_scores_gemma":[0.9996321,0.0001115435,0.0000390368,0.00009874508,0.0001075127,0.00001104414],"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":[0.0001272137,0.0002030484,0.003721359,0.0006232379,0.0001013293,0.0002056536,0.0003357827,0.6750623,0.118891,0.0647596,0.004139264,0.1318301],"study_design_scores_gemma":[0.00003758227,0.00008670741,0.0004440491,0.00003362266,0.00001667264,0.00003362132,0.00008590001,0.9662135,0.02290931,0.006540664,0.003568753,0.00002956495],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3691865,0.0009604826,0.6174583,0.0004397397,0.00009288348,0.0002885267,0.001501355,0.001364354,0.008707881],"genre_scores_gemma":[0.3767843,0.001219934,0.617579,0.0001028105,0.00002220339,0.0003529864,0.002332025,0.0003564943,0.001250213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007401206,"threshold_uncertainty_score":0.01471621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01020929609971499,"score_gpt":0.2537151730596316,"score_spread":0.2435058769599166,"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."}}