Related Experiment Video
Updated: Dec 11, 2025

Hybrid µCT-FMT imaging and image analysis
Published on: June 4, 2015
A DFT/machine-learning hybrid method for the prediction of 3 JHCCH couplings
1Departamento de Química Fundamental, CCEN, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil.
Abstract:
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 JHH couplings with accuracy comparable or better than the well-known Altona equation, and it can provide sensible 3 JHH predictions in systems not well handled by the Altona equation such as epoxide or cyclopropane rings.
Related Concept Videos
Spin–Spin Coupling: Three-Bond Coupling (Vicinal Coupling)
The extent of coupling depends on the C‑C bond length, the two H‑C‑C angles, any electron-withdrawing substituents, and the dihedral angle between the involved orbitals. The...
Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)
The central atom need not be NMR-active because its electrons are affected by the electron polarization of the spin-active atoms. However, spin information is transmitted less effectively than in one-bond coupling, and 2J values are usually weaker than 1J values. The energy of...
Spin–Spin Coupling Constant: Overview
Qualitatively, any spin plus-half nucleus polarizes the spins of its electrons to the minus-half state. Consequently, the paired electron in the hydrogen–carbon bond must...
Predicting Molecular Geometry
Hybridization of Atomic Orbitals II
Spin–Spin Coupling: One-Bond Coupling

