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Related Concept Videos

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...
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Paramagnetism01:30

Paramagnetism

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Paramagnets are materials with unpaired electrons that possess a finite magnetic moment. In the absence of a magnetic field, these moments are randomly oriented, and thus the net moment is zero. Under an external field, a torque acting on the moments tends to align them along the field's direction. However, the random thermal motion of electrons produces a torque opposite to the external field and tries to disorient the moments. These two competing effects align only a few moments along the...
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Magnetic Susceptibility and Permeability01:31

Magnetic Susceptibility and Permeability

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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...
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Diamagnetism01:26

Diamagnetism

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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....
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Colors and Magnetism03:02

Colors and Magnetism

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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...
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Related Experiment Video

Updated: May 23, 2025

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses
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Data-Driven Design of Mechanically Hard Soft Magnetic High-Entropy Alloys.

Mian Dai1, Yixuan Zhang1, Xiaoqing Li2

  • 1Institute of Materials Science, Technical University of Darmstadt, Alarich-Weiss-Str. 2, Darmstadt, Germany.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|March 26, 2025
PubMed
Summary

This study introduces a data-driven framework to discover novel high-entropy alloys (HEAs) with both mechanical hardness and magnetic softness. The approach accelerates the design of advanced materials for technological applications.

Keywords:
density functional theoryhigh‐entropy alloyshigh‐throughput calculationsmachine learningmechanically hard soft magnets

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Alloy Design

Background:

  • Developing materials with both high hardness and soft magnetic properties is crucial for advanced technologies.
  • Exploring the vast compositional space of high-entropy alloys (HEAs) presents a significant challenge.

Purpose of the Study:

  • To present a data-driven framework for discovering multifunctional HEAs.
  • To identify HEA candidates optimized for high hardness and magnetic softness.

Main Methods:

  • Utilized a large dataset of 1,842,628 density functional theory calculations.
  • Analyzed quaternary and quinary equimolar HEAs derived from 42 elements.
  • Employed ensemble learning and predictive models to correlate composition, structure, and properties.

Main Results:

  • Established predictive models linking alloy composition to mechanical and magnetic properties.
  • Demonstrated a framework for efficient exploration of HEA compositional space.
  • Identified promising HEA candidates for multifunctional applications.

Conclusions:

  • Data-driven strategies significantly accelerate the discovery of advanced materials.
  • The presented framework enables the design of next-generation alloys with desired hardness and magnetic softness.
  • Highlights the potential of computational approaches in materials innovation.