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Related Concept Videos

Atomic Nuclei: Types of Nuclear Relaxation01:28

Atomic Nuclei: Types of Nuclear Relaxation

253
Nuclear relaxation restores the equilibrium population imbalance and can occur via spin–lattice or spin–spin mechanisms, which are first-order exponential decay processes.
In spin–lattice or longitudinal relaxation, the excited spins exchange energy with the surrounding lattice as they return to the lower energy level. Among several mechanisms that contribute to spin–lattice relaxation, magnetic dipolar interactions are significant. Here, the excited nucleus transfers...
253
Atomic Nuclei: Nuclear Relaxation Processes01:23

Atomic Nuclei: Nuclear Relaxation Processes

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In the absence of an external magnetic field, nuclear spin states are degenerate and randomly oriented. When a magnetic field is applied, the spins begin to precess and orient themselves along (lower energy) or against (higher energy) the direction of the field. At equilibrium, a slight excess population of spins exists in the lower energy state. Because the direction of the magnetic field is fixed as the z-axis,  the precessing magnetic moments are randomly oriented around the z-axis.
622
Atomic Nuclei: Nuclear Spin State Overview01:03

Atomic Nuclei: Nuclear Spin State Overview

866
NMR-active nuclei have energy levels called 'spin states' that are associated with the orientations of their nuclear magnetic moments. In the absence of a magnetic field, the nuclear magnetic moments are randomly oriented, and the spin states are degenerate. When an external magnetic field is applied, the spin states have only 2 + 1 orientations available to them. A proton with = ½ has two available orientations. Similarly, for a quadrupolar nucleus with a nuclear spin value of...
866
Spin–Spin Coupling Constant: Overview01:08

Spin–Spin Coupling Constant: Overview

876
In bromoethane, the three methyl protons are coupled to the two methylene protons that are three bonds away. In accordance with the n+1 rule, the signal from the methyl protons is split into three peaks with 1:2:1 relative intensities. The methylene protons appear as a quartet, with the relative intensities of 1:3:3:1.
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...
876
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.0K
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.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.0K
Atomic Nuclei: Nuclear Spin State Population Distribution01:14

Atomic Nuclei: Nuclear Spin State Population Distribution

938
Near absolute zero temperatures, in the presence of a magnetic field, the majority of nuclei prefer the lower energy spin-up state to the higher energy spin-down state. As temperatures increase, the energy from thermal collisions distributes the spins more equally between the two states. The Boltzmann distribution equation gives the ratio of the number of spins predicted in the spin −½ (N−) and spin +½ (N+) states.
938

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Measuring the Spin-Lattice Relaxation Magnetic Field Dependence of Hyperpolarized [1-13C]pyruvate
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Machine Learning Mapping Approach for Computing Spin Relaxation Dynamics.

Mohammad Shakiba1, Adam B Philips1, Jochen Autschbach1

  • 1Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States.

The Journal of Physical Chemistry Letters
|December 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning method to predict atomic system properties. It accurately forecasts electric field gradients and spin relaxation rates using less data than traditional calculations.

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Area of Science:

  • Computational chemistry and materials science.
  • Application of machine learning in quantum mechanics.
  • Method development for predicting molecular properties.

Background:

  • Predicting properties of atomistic systems often requires computationally expensive high-level theory calculations.
  • Electric field gradients (EFGs) are crucial for understanding nuclear properties and spin relaxation.
  • Existing methods for calculating EFGs and spin relaxation rates can be time-consuming.

Purpose of the Study:

  • To develop and validate a machine learning (ML) approach for predicting electric field gradient (EFG) tensors.
  • To utilize ML-predicted EFGs for calculating spin relaxation rates of ions in aqueous solutions.
  • To demonstrate the efficiency of the ML approach in terms of data requirements.

Main Methods:

  • A machine learning mapping approach was employed, using atomic orbital overlap, density, or Kohn-Sham (KS) Fock matrix elements from extended tight-binding as input features.
  • These features were used to predict EFG tensors, which are typically obtained at higher levels of theory (e.g., hybrid functionals).
  • The predicted EFG tensors were then used to compute quadrupolar isotropic spin relaxation rates for various ions.

Main Results:

  • The ML approach successfully predicted EFG tensors with high accuracy.
  • The predicted EFG tensors enabled accurate computation of spin relaxation rates for several ions in aqueous solutions.
  • The method achieved good accuracy (2-8% relative error) for spin relaxation rates using only a fraction of the data required for direct calculations.

Conclusions:

  • Machine learning offers an efficient and accurate alternative for predicting EFG tensors and spin relaxation rates.
  • This approach significantly reduces the computational cost and data requirements compared to traditional methods.
  • The developed ML model holds promise for accelerating materials discovery and understanding molecular dynamics.