化学位移
密度泛函理论
绝对偏差
集合(抽象数据类型)
相对标准差
材料科学
碳-13核磁共振
计算化学
数据集
化学
质子
混合功能
物理
算法
计算机科学
水准点(测量)
数学
物理化学
核磁共振
统计
人工智能
核物理学
检出限
大地测量学
地理
程序设计语言
作者
Marcelo Tavares de Oliveira,Júlia M. A. Alves,Ataualpa Albert Carmo Braga,David J. D. Wilson,Cristina A. Barboza
标识
DOI:10.1021/acs.jctc.1c00604
摘要
A benchmark density functional theory (DFT) study of 1H NMR chemical shifts for data sets comprising 200 chemical shifts, including complex natural products, has been carried out to assess the performance of DFT methods. Two new benchmark data sets, NMRH33 and NMRH148, have been established. The meta-GGA revTPSS performs remarkably well against the NMRH33 benchmark set (mean absolute deviation (MAD), 0.10 ppm; maximum deviation (max), 0.26 ppm) with the smallest MAD of all evaluated functionals. The best-performing double-hybrid density functional (DHDF), revDSD-BLYP (MAD, 0.16 ppm; max, 0.35 ppm), performs similarly to hybrid-GGA methods (e.g., mPW1PW91/6-311G(d) (MAD, 0.15 ppm; max, 0.36 ppm)), but at a considerably higher computational cost. The results indicate that currently available double-hybrid DFT methods offer no benefit over GGA (including hybrid and meta) functionals in the calculation of 1H NMR chemical shifts.
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