Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Atomic Nuclei: Nuclear Spin State Overview01:03

Atomic Nuclei: Nuclear Spin State Overview

1.4K
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...
1.4K
Atomic Nuclei: Nuclear Relaxation Processes01:23

Atomic Nuclei: Nuclear Relaxation Processes

860
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.
860
Magnetic Vector Potential01:15

Magnetic Vector Potential

1.2K
In electrostatics, the electric field can be written as the negative gradient of the potential. In magnetostatics, the zero divergence of the magnetic field ensures that the magnetic field can be expressed as the curl of a vector potential. This potential is known as the magnetic vector potential.
Consider an ideal solenoid with n turns per unit length and radius R. If I is the current through the solenoid, the magnetic field inside the solenoid is expressed as the product of vacuum...
1.2K
Atomic Nuclei: Nuclear Magnetic Moment00:59

Atomic Nuclei: Nuclear Magnetic Moment

2.4K
All atomic nuclei are positively charged. When they have a nonzero spin, they behave like rotating charges. As a consequence of their charge and spin, these nuclei generate a magnetic field (B). This, in turn, gives rise to a magnetic moment (μ), which is randomly oriented in the absence of an external magnetic field. When an external magnetic field (B0) is applied, the magnetic moment vectors can align with the field or against it in 2 + 1 orientations. A hydrogen nucleus, which is just a...
2.4K
Magnetic Fields01:27

Magnetic Fields

6.5K
A moving charge or a current creates a magnetic field in the surrounding space, in addition to its electric field. The magnetic field exerts a force on any other moving charge or current that is present in the field. Like an electric field, the magnetic field is also a vector field. At any position, the direction of the magnetic field is defined as the direction in which the north pole of a compass needle points.
A magnetic field is defined by the force that a charged particle experiences...
6.5K
Valence Bond Theory02:42

Valence Bond Theory

9.9K
Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...
9.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Development of a Machine Learning-Based Triage Score for Medication-Related Osteonecrosis of the Jaw in Osteoporosis Patients Undergoing Tooth Extraction.

Diagnostics (Basel, Switzerland)·2026
Same author

Efficacy of Polynucleotide for the Recovery of Depressed Lesions Following Steroid-Based Lipolysis Injections.

Clinical, cosmetic and investigational dermatology·2026
Same author

Real-space imaging and control of topological spin textures in a van der Waals antiferromagnet.

Nature communications·2026
Same author

Mapping brain function underlying naturalistic motor observation and imitation using high-density diffuse optical tomography.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Unusual Ferromagnetic Band Evolution and High Curie Temperature in Monolayer 1T-CrTe<sub>2</sub> on Bilayer Graphene.

Small (Weinheim an der Bergstrasse, Germany)·2025
Same author

Skin-Whitening, Antiwrinkle, and Moisturizing Effects of <i>Astilboides tabularis</i> (Hemsl.) Engl. Root Extracts in Cell-Based Assays and Three-Dimensional Artificial Skin Models.

International journal of molecular sciences·2025

Related Experiment Video

Updated: Nov 2, 2025

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains
07:42

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

Published on: July 20, 2022

3.0K

Magnetic State Generation using Hamiltonian Guided Variational Autoencoder with Spin Structure Stabilization.

Hee Young Kwon1, Han Gyu Yoon2, Sung Min Park2

  • 1Center for Spintronics, Korea Institute of Science and Technology, Seoul, 02792, South Korea.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|June 9, 2021
PubMed
Summary

Researchers developed a Hamiltonian-guided generative model using a variational autoencoder to create stable magnetic states. This approach enhances numerical simulations for exploring new physical systems and predicting their properties.

Keywords:
energy minimizationgenerative modelmachine learningmicromagnetismthe ground state

More Related Videos

Experimental Methods for Spin- and Angle-Resolved Photoemission Spectroscopy Combined with Polarization-Variable Laser
09:00

Experimental Methods for Spin- and Angle-Resolved Photoemission Spectroscopy Combined with Polarization-Variable Laser

Published on: June 28, 2018

10.2K
Chemical Vapor Deposition of an Organic Magnet, Vanadium Tetracyanoethylene
08:25

Chemical Vapor Deposition of an Organic Magnet, Vanadium Tetracyanoethylene

Published on: July 3, 2015

11.7K

Related Experiment Videos

Last Updated: Nov 2, 2025

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains
07:42

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

Published on: July 20, 2022

3.0K
Experimental Methods for Spin- and Angle-Resolved Photoemission Spectroscopy Combined with Polarization-Variable Laser
09:00

Experimental Methods for Spin- and Angle-Resolved Photoemission Spectroscopy Combined with Polarization-Variable Laser

Published on: June 28, 2018

10.2K
Chemical Vapor Deposition of an Organic Magnet, Vanadium Tetracyanoethylene
08:25

Chemical Vapor Deposition of an Organic Magnet, Vanadium Tetracyanoethylene

Published on: July 3, 2015

11.7K

Area of Science:

  • Physics
  • Materials Science
  • Computational Science

Background:

  • Numerical generation of physical states is crucial for scientific research, aiding in understanding experimental results and predicting properties of novel systems.
  • Traditional methods may face limitations in exploring complex systems like magnetic states.

Purpose of the Study:

  • To devise and apply a variational autoencoder model for generating energetically stable magnetic states with minimal local deformation.
  • To leverage an explicit magnetic Hamiltonian within the training process for spin structure stabilization.

Main Methods:

  • A variational autoencoder model was developed and applied to a magnetic system.
  • The model incorporated an explicit magnetic Hamiltonian to minimize energy during training, ensuring spin structure stabilization.
  • The generative model was trained to produce energetically stable magnetic states.

Main Results:

  • The model successfully generated energetically stable magnetic states with low local deformation.
  • The Hamiltonian-guided approach enabled the generator to produce long-range ordered ground states, even when not explicitly included in the training data.
  • The stabilization role could be increased to achieve desired spin configurations.

Conclusions:

  • The proposed Hamiltonian-guided generative model offers a powerful new tool for numerical approaches in scientific research.
  • This method advances the generation of stable physical states, particularly in magnetic systems.
  • The model's ability to predict uncharted systems and their characteristics holds significant potential for future scientific discovery.