With continuing breakthroughs in computational chemistry theory and substantial improvement of computation hardware performance, great progresses have been made in recent years in first principle-based prediction of 1H and 13C chemical shifts of organic molecules. Some methods have even been gradually applied for accurate prediction in complex molecular systems. In this paper, a density functional theory-based high-precision on-line chemical shift prediction platform for organic molecules is established, which provides on-line interactive service of chemical shift prediction for molecules with a molecular weight less than 800. The platform accelerates the mapping between nuclear magnetic resonance (NMR) spectra and molecular structures, and provides a powerful tool for efficient assignment of NMR spectra and accurate analysis of organic molecular structure.
LI Qian
,
TANG Ya-lin
,
XIANG Jun-feng
. An on-Line NMR Chemical Shift Prediction Platform Based on Density Functional Theory[J]. Chinese Journal of Magnetic Resonance, 2021
, 38(1)
: 22
-31
.
DOI: 10.11938/cjmr20202831
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