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Related Concept Videos

Double Resonance Techniques: Overview01:12

Double Resonance Techniques: Overview

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Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
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Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)01:20

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Two NMR-active nuclei bonded to a central atom can be involved in geminal or two-bond coupling. Geminal coupling is commonly seen between diastereotopic protons in chiral molecules and unsymmetrical alkenes, among others.
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...
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Two-Step Machine Learning Approach for Charge-Transfer Coupling with Structurally Diverse Data.

Hung-Hsuan Lin1,2, Chun-I Wang1,3, Chou-Hsun Yang1

  • 1Institute of Chemistry, Academia Sinica, 128 Section 2 Academia Road, Nankang, Taipei 115, Taiwan.

The Journal of Physical Chemistry. A
|December 29, 2023
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Summary
This summary is machine-generated.

We developed a new machine learning method to accurately predict electronic coupling in molecular pairs. This approach improves charge-transfer rate predictions, especially for complex amorphous systems.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Machine Learning

Background:

  • Electronic coupling dictates charge-transfer rates, influenced by intermolecular and intramolecular factors.
  • Accurate prediction is difficult, particularly for amorphous materials with diverse configurations.

Purpose of the Study:

  • To develop a robust machine learning algorithm for predicting electronic coupling in molecular pairs.
  • To enhance the accuracy of charge-transfer dynamics modeling.

Main Methods:

  • A two-step Kernel Ridge Regression (KRR) model was employed.
  • The first KRR model predicts molecular orbitals (MOs) from structural variations.
  • The second KRR model predicts coupling strength using MO overlap integrals.

Main Results:

  • Achieved mean absolute errors of 0.27 meV for ethylene dimers and 1.99 meV for naphthalene pairs.
  • Demonstrated significant error reductions (14-fold and 3-fold, respectively).
  • Predicted MOs can serve as input for other quantum chemical property calculations.

Conclusions:

  • The novel two-step KRR approach significantly improves electronic coupling prediction accuracy.
  • This method offers a more reliable way to model charge-transfer dynamics, especially in amorphous systems.
  • The approach is adaptable and compatible with other accurate MO predictors.