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Related Concept Videos

Atomic Nuclei: Nuclear Spin State Overview01:03

Atomic Nuclei: Nuclear Spin State Overview

1.8K
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 one, the...
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Atomic Nuclei: Nuclear Spin State Population Distribution01:14

Atomic Nuclei: Nuclear Spin State Population Distribution

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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.
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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NMR Spectroscopy: Spin–Spin Coupling01:08

NMR Spectroscopy: Spin–Spin Coupling

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The spin state of an NMR-active nucleus can have a slight effect on its immediate electronic environment. This effect propagates through the intervening bonds and affects the electronic environments of NMR-active nuclei up to three bonds away; occasionally, even farther. This phenomenon is called spin–spin coupling or J-coupling. Coupling interactions are mutual and result in small changes in the absorption frequencies of both nuclei involved. While nuclei of the same element are involved...
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Related Experiment Video

Updated: Dec 25, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

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Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions.

Michael G Taylor1, Tzuhsiung Yang1, Sean Lin1

  • 1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

The Journal of Physical Chemistry. A
|April 1, 2020
PubMed
Summary
This summary is machine-generated.

Assigning ground-state spins in transition-metal complexes is crucial for catalysis and materials science. This study uses artificial neural networks (ANNs) trained on crystal structures to predict spins, offering a cost-effective alternative to traditional methods.

Related Experiment Videos

Last Updated: Dec 25, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.2K

Area of Science:

  • Inorganic Chemistry
  • Computational Chemistry
  • Materials Science

Background:

  • Determining ground-state spins of open-shell transition-metal complexes is vital for understanding their catalytic and materials properties.
  • Approximate electronic structure methods often struggle with accurate spin-state determination.
  • Experimental crystal structures offer a rich source of geometric information.

Purpose of the Study:

  • To develop a structure-based method for assigning ground-state spins of transition-metal complexes.
  • To overcome limitations of simple distance-based heuristics using advanced machine learning.
  • To provide a computationally inexpensive complement to traditional energy-based spin-state assignment methods.

Main Methods:

  • Analysis of metal-ligand bond length distributions in over 2000 mononuclear Fe(II)/Fe(III) complexes.
  • Development and application of artificial neural networks (ANNs) to predict spin-state-dependent bond lengths.
  • Classification of experimental ground-state spins by matching experimental structures to ANN predictions.

Main Results:

  • Identified limitations of distance-based heuristics for spin assignment.
  • Achieved accurate ground-state spin assignment for 80-90% of analyzed structures using ANNs.
  • Successfully classified over 95% of spin states in a dataset of 46 Fe(II) spin-crossover complexes.

Conclusions:

  • Crystal structure data, when analyzed with ANNs, can reliably predict ground-state spins in transition-metal complexes.
  • This structure-based machine learning approach offers a practical and efficient alternative to computational electronic structure methods.
  • The method shows significant promise for data-mining literature and identifying spin-crossover materials.