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Related Concept Videos

Magnetic Fields01:27

Magnetic Fields

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...
Ferromagnetism01:31

Ferromagnetism

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...
Magnetic Susceptibility and Permeability01:31

Magnetic Susceptibility and Permeability

In linear magnetic materials, like paramagnets and diamagnets, magnetization is proportional to the magnetic field intensity. The constant of proportionality, a dimensionless number, is called magnetic susceptibility. The value of the susceptibility depends on the type of material.
When diamagnetic materials are placed under an external magnetic field, the moments opposite to the field are induced. Hence, the susceptibility for diamagnets has a minimal negative value of 10-5–10-6. Since...
Magnetism01:30

Magnetism

Magnets are commonly found in everyday objects, such as toys, hangers, elevators, doorbells, and computer devices. Experimentation on these magnets shows that all magnets have two poles: one is labeled north (N) and the other south (S). Magnetic poles repel if they are alike and attract if unlike. Moreover, both poles of a magnet attract unmagnetized pieces of iron.
An individual magnetic pole cannot be isolated. No matter how small, every piece of a magnet contains a north pole and a south...
Diamagnetism01:26

Diamagnetism

Materials consisting of paired electrons have zero net magnetic moments. However, when these materials are placed under an external magnetic field, the moments opposite to the field are induced. Such materials are called diamagnets. Diamagnetism is the response of the diamagnets when placed in an external magnetic field.
Diamagnetism was discovered by Anton Brugmans in 1778 when he observed that bismuth gets repelled by magnetic fields, thus theorizing that diamagnets get repelled by magnets.
Motional Emf01:22

Motional Emf

Magnetic flux depends on three factors: the strength of the magnetic field, the area through which the field lines pass, and the field's orientation with respect to the surface area. If any of these quantities vary, a corresponding variation in magnetic flux occurs. If the area through which the magnetic field lines are passing changes, then the magnetic flux also changes. This change in the area can be of two types: the flux through the rectangular loop increases as it moves into the magnetic...

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A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings
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Machine Learning Model for Nd2Fe14B-Based Permanent Magnets.

Manuel Enns1, Wolfgang Körner1, Daniel F Urban1,2

  • 1Fraunhofer Institute for Mechanics of Materials IWM, Wöhlerstr. 11, 79108 Freiburg, Germany.

Materials (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

This study developed an efficient machine learning model to predict magnetic property changes in neodymium iron boron magnets with various impurities. The model accurately captures complex magnetic property variations, aiding in magnet recycling efforts.

Keywords:
density functional theorymachine learningpermanent magnets

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Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples
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Published on: June 9, 2016

Area of Science:

  • Materials Science
  • Computational Materials Science
  • Magnetism

Background:

  • Neodymium iron boron (Nd2Fe14B) magnets are critical for modern technologies.
  • Increasing demand for magnet recycling necessitates understanding the impact of diverse impurities.
  • Impurity elements like Cr, Mn, and Sm complicate magnetic property prediction.

Purpose of the Study:

  • To develop an efficient machine learning (ML) model for predicting magnetic property changes in Nd2Fe14B magnets.
  • To accurately predict alterations in saturation magnetization (Ms) and magnetocrystalline anisotropy (K1).
  • To create a versatile model capable of handling numerous substitutional elements and concentrations.

Main Methods:

  • Implementation of an efficient machine learning model.
  • Utilizing feature engineering to address challenges posed by specific impurity elements.
  • Training a single, comprehensive model for diverse impurity scenarios.

Main Results:

  • Demonstrated the sufficiency of relatively simple ML models for complex magnetic property prediction.
  • Successfully predicted magnetic property changes across a wide range of impurity elements (>20).
  • Developed a unified model effective for various impurity concentrations.

Conclusions:

  • The developed ML model efficiently predicts magnetic property changes in Nd2Fe14B magnets with impurities.
  • Feature engineering effectively mitigates challenges from complex magnetic elements, enabling a single predictive model.
  • This approach supports the recycling of permanent magnets by predicting property variations due to impurities.