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Related Concept Videos

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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.
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A stationary charge creates and interacts with the electric field, while a moving charge creates a magnetic field.
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A solenoid is a conducting wire coated with an insulating material, wound tightly in the form of a helical coil. The magnetic field due to a solenoid is the vector sum of the magnetic fields due to its individual turns. Therefore, for an ideal solenoid, the magnetic field within the solenoid is directly proportional to the number of turns per unit length and the current. Conversely, the magnetic field outside the solenoid is zero.
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Energy In A Magnetic Field01:24

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If a magnetic field is sustained, there must be a current in a closed circuit or loop, implying some energy has been spent in creating the field. If this energy is not dissipated via the circuit's resistance, it is stored in the field.
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Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples
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An innovative magnetic state generator using machine learning techniques.

H Y Kwon1,2, N J Kim1, C K Lee1

  • 1Department of Physics, Kyung Hee University, Seoul, 02447, South Korea.

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Summary
This summary is machine-generated.

We developed an efficient algorithm using a complex-valued neural network to simulate magnetic structures. This method successfully determines ground spin configurations, even for complex chiral magnetic systems.

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Area of Science:

  • Computational physics
  • Materials science
  • Machine learning

Background:

  • Simulating magnetic structures is crucial for understanding materials.
  • Existing methods can be computationally intensive.
  • Developing efficient numerical algorithms is an ongoing challenge.

Purpose of the Study:

  • To introduce a novel, efficient algorithm for numerical simulation of magnetic structures.
  • To leverage generative models and neural networks for magnetic structure prediction.
  • To demonstrate the algorithm's capability in determining ground spin configurations.

Main Methods:

  • Utilizing a complex-valued neural network to generate k-space information.
  • Employing hermitization and inverse fast Fourier transform for real-space spin configurations.
  • Minimizing magnetic energy using the Adam optimization algorithm.

Main Results:

  • The algorithm achieves outstanding performance in finding proper ground spin configurations.
  • Successfully applied to solve spin configurations of magnetic chiral structures in model cases.
  • Demonstrated the ability to obtain magnetic long-range order irrespective of system size.

Conclusions:

  • The proposed algorithm offers an efficient and effective approach for simulating magnetic structures.
  • It shows promise for complex systems, including magnetic chiral structures.
  • The method is scalable and capable of capturing long-range magnetic order.