{"id":"W3107136152","doi":"10.1021/acs.jpca.0c06372","title":"Dispersion-Corrected DFT Methods for Applications in Nuclear Magnetic Resonance Crystallography","year":2020,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry A","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Genentech; Florida State University","keywords":"Electric field gradient; Neutron diffraction; Density functional theory; Chemistry; Tensor (intrinsic definition); Crystal structure; Neutron spectroscopy; Nuclear magnetic resonance; Neutron; Molecular physics; Computational chemistry; Crystallography; Physics; Electric field; Neutron scattering; Quantum mechanics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.00009950199,0.0001231045,0.0002190924,0.000009777867,0.00008471902,0.00001128673,0.0004664508,0.00005417667,0.00008762477],"category_scores_gemma":[0.00009144646,0.00008912074,0.0001848904,0.0002924437,0.0001042004,0.00005000582,0.00005544117,0.0003619952,9.957349e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002830915,"about_ca_system_score_gemma":0.00002067679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001316718,"about_ca_topic_score_gemma":8.883851e-8,"domain_scores_codex":[0.999318,0.00001661047,0.0002726374,0.0001329584,0.0001060553,0.0001537558],"domain_scores_gemma":[0.999015,0.0003752174,0.0001966182,0.0002192321,0.00008924407,0.0001047433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009502185,0.0001383412,0.000009996833,0.00007654556,0.000007152334,4.348318e-7,0.0002941821,0.0003037525,0.985592,0.0002289174,0.0003426264,0.012911],"study_design_scores_gemma":[0.000893761,0.0001255454,0.00007134134,0.0001046309,0.0001302169,0.0000219514,0.001058603,0.03982451,0.5566445,0.009748908,0.3910567,0.0003193949],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.431915,0.005011305,0.5398647,0.00902668,0.00001115164,0.001110909,0.0001983523,0.0003546047,0.01250731],"genre_scores_gemma":[0.9771722,0.0001184092,0.02186038,0.0001923612,0.0004422569,0.00006726185,0.000008174536,0.00003489559,0.0001040768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5452572,"threshold_uncertainty_score":0.3634238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01325584716397508,"score_gpt":0.3122760763160867,"score_spread":0.2990202291521116,"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."}}