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

Electronic Structure of Atoms02:28

Electronic Structure of Atoms

21.5K

An atom comprises protons and neutrons, which are contained inside the dense, central core called the nucleus, with electrons present around the nucleus. Taking into account the wave–particle duality of electrons and the uncertainty in position around the nucleus, quantum mechanics provides a more accurate model for the atomic structure. It describes atomic orbitals as the regions around the nucleus where electrons of discrete energy exist, characterized by four quantum...
21.5K
Colors and Magnetism03:02

Colors and Magnetism

11.9K
Color in Coordination Complexes
When atoms or molecules absorb light at the proper frequency, their electrons are excited to higher-energy orbitals. For many main group atoms and molecules, the absorbed photons are in the ultraviolet range of the electromagnetic spectrum, which cannot be detected by the human eye. For coordination compounds, the energy difference between the d orbitals often allows photons in the visible range to be absorbed and emitted, which is seen as colors by the human...
11.9K
Ferromagnetism01:31

Ferromagnetism

2.4K
Materials like iron, nickel, and cobalt consist of magnetic domains, within which the magnetic dipoles are arranged parallel to each other. The magnetic dipoles are rigidly aligned in the same direction within a domain by quantum mechanical coupling among the atoms. This coupling is so strong that even thermal agitation at room temperature cannot break it. The result is that each domain has a net dipole moment. However, some materials have weaker coupling, and are ferromagnetic at lower...
2.4K
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.1K
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...
1.1K
Atomic Nuclei: Nuclear Spin State Overview01:03

Atomic Nuclei: Nuclear Spin State Overview

1.0K
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...
1.0K
The Pauli Exclusion Principle03:06

The Pauli Exclusion Principle

39.1K
The arrangement of electrons in the orbitals of an atom is called its electron configuration. We describe an electron configuration with a symbol that contains three pieces of information:
39.1K

You might also read

Related Articles

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

Sort by
Same author

Metal nanoparticle-mediated photothermal therapy for bacterial eradication: Mechanisms, strategies, and clinical challenges.

Colloids and surfaces. B, Biointerfaces·2026
Same author

Intertwined orders in a quantum-entangled metal.

Nature materials·2026
Same author

Ligand-modified liposomes as drug delivery systems for the active targeting of pancreatic cancer.

International journal of pharmaceutics: X·2026
Same author

Various Topological Poly(tert-butyl acrylate)s and Their Impacts on Thermal and Solution Properties.

Macromolecular rapid communications·2025
Same author

Selective fluorescent probe for Tl<sup>3+</sup> ions through metal-induced hydrolysis and its application for direct assay of artificial urine.

RSC advances·2025
Same author

New Insights into AMPK, as a Potential Therapeutic Target in Metabolic Dysfunction-Associated Steatotic Liver Disease and Hepatic Fibrosis.

Biomolecules & therapeutics·2024

Related Experiment Video

Updated: Jul 20, 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

2.8K

Classification of magnetic order from electronic structure by using machine learning.

Yerin Jang1, Choong H Kim2,3, Ara Go4

  • 1Department of Physics, Chonnam National University, Gwangju, 61186, Korea.

Scientific Reports
|August 1, 2023
PubMed
Summary

Machine learning now identifies magnetic states from excitation spectra, overcoming neutron scattering limits. This approach accurately classifies antiferromagnetic order using spectral data and excitation energies.

More Related Videos

Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins
07:24

Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins

Published on: September 23, 2021

1.8K
Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks
06:53

Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks

Published on: June 9, 2023

2.0K

Related Experiment Videos

Last Updated: Jul 20, 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

2.8K
Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins
07:24

Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins

Published on: September 23, 2021

1.8K
Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks
06:53

Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks

Published on: June 9, 2023

2.0K

Area of Science:

  • Condensed Matter Physics
  • Materials Science
  • Computational Physics

Background:

  • Identifying magnetic states is crucial for material applications.
  • Neutron scattering experiments have limitations for direct magnetic state identification.
  • Developing alternative methods for magnetic state identification is essential.

Purpose of the Study:

  • To develop a machine-learning approach for identifying magnetic states from spin-integrated excitation spectra.
  • To utilize decision-tree algorithms for classifying antiferromagnetic order.
  • To explore the effectiveness of spectral data and excitation energies as features for machine learning.

Main Methods:

  • Generated a dataset using Hartree-Fock mean-field calculations on a Wannier Hamiltonian.
  • Extracted spectral data from first-principle calculations for BaOsO[Formula: see text].
  • Trained decision-tree machine learning models using local density of states, momentum-resolved density of states, and excitation energies.

Main Results:

  • The machine learning model successfully identified antiferromagnetic order from spectral data.
  • Broadening methods significantly impacted model performance.
  • Incorporating excitation energy as a feature improved classification accuracy, even for diverse test samples.

Conclusions:

  • Machine learning offers a viable alternative for identifying magnetic states.
  • Excitation energy is a valuable feature for improving magnetic state classification.
  • The developed approach demonstrates robustness across different data generation methods.