Journal
MAGNETIC RESONANCE IN CHEMISTRY
Volume 59, Issue 4, Pages 414-422Publisher
WILEY
DOI: 10.1002/mrc.5087
Keywords
DFT; machine learning; prediction; vicinal couplings
Funding
- Fundacao de Amparo a Ciencia e Tecnologia do Estado de Pernambuco [APQ-1864-1.06/12]
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico [426216/2018-0, 311683/2019-3]
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A machine learning model has been developed for predicting vicinal proton-proton couplings with accuracy comparable or better than the Altona equation, especially in systems like epoxide or cyclopropane rings where the Altona equation may not perform well.
A machine learning model for the prediction of vicinal proton-proton couplings has been developed based on a hybrid representation that includes geometrical and electronic parameters derived from natural bond orbital (NBO) analysis of low-level BLYP/STO-3G computations. The model can predict(3)J(HH)couplings with accuracy comparable or better than the well-known Altona equation, and it can provide sensible(3)J(HH)predictions in systems not well handled by the Altona equation such as epoxide or cyclopropane rings.
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