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Metallic Solids02:37

Metallic Solids

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Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
19.7K
Ferromagnetism01:31

Ferromagnetism

2.6K
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...
2.6K
Theory of Metallic Conduction01:17

Theory of Metallic Conduction

1.5K
The conduction of free electrons inside a conductor is best described by quantum mechanics. However, a classical model makes predictions close to the results of quantum mechanics. It is called the theory of metallic conduction.
In this theory, Newton's second law of motion is used to determine the acceleration of an electron in the presence of an applied electric field. Then, its velocity is expressed via this acceleration.
An electron moves through the crystal, containing positive ions,...
1.5K
Network Covalent Solids02:18

Network Covalent Solids

15.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
15.2K
Mechanical Characteristics of Steel01:18

Mechanical Characteristics of Steel

825
The mechanical characteristics of steel are assessed through various tests that evaluate its strength, toughness, and flexibility. These tests include tension, torsion, impact, bending, and hardness assessments, each providing crucial information about steel's suitability for specific applications.
The tension test is fundamental for determining tensile strength. In this test, a steel specimen is stretched using a gripping device until it breaks. The data collected during this test are used...
825
Trends in Lattice Energy: Ion Size and Charge02:54

Trends in Lattice Energy: Ion Size and Charge

25.4K
An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:
25.4K

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Related Experiment Video

Updated: Oct 26, 2025

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
09:41

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

Published on: May 29, 2018

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Neural evolution structure generation: High entropy alloys.

Conrard Giresse Tetsassi Feugmo1, Kevin Ryczko2, Abu Anand3

  • 1National Research Council Canada, Ottawa, Ontario K1A 0R6, Canada.

The Journal of Chemical Physics
|August 3, 2021
PubMed
Summary
This summary is machine-generated.

We developed a neural evolution structure (NES) method using artificial neural networks and evolutionary algorithms. This approach efficiently generates large, high-entropy alloy structures, significantly reducing computational time and costs compared to traditional methods.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Alloy Design

Background:

  • Generating high-entropy alloy (HEA) structures computationally is challenging due to the large number of atoms and complex configurations.
  • Special Quasi-Random Structures (SQSs) are a common method but are computationally intensive and limited in scale.
  • Developing efficient methods for generating large, representative HEA structures is crucial for materials discovery.

Purpose of the Study:

  • To introduce a novel Neural Evolution Structure (NES) generation methodology.
  • To enable the efficient and scalable generation of high-entropy alloy structures.
  • To reduce the computational cost and time associated with generating large alloy structures.

Main Methods:

  • Combining artificial neural networks and evolutionary algorithms for inverse design.
  • Utilizing pair distribution functions and atomic properties as the basis for the model.
  • Training the model on smaller unit cells to generate larger, representative structures.

Main Results:

  • Achieved a speed-up factor of approximately 1000 compared to SQS methods.
  • Enabled the generation of very large structures (over 40,000 atoms) within hours.
  • Demonstrated the ability of a single model to generate multiple unique structures with the same composition, unlike SQSs.

Conclusions:

  • The NES methodology offers a significant advancement in generating high-entropy alloy structures.
  • This approach drastically reduces computational demands, making large-scale HEA structure generation feasible.
  • NES provides a versatile and efficient tool for accelerating materials discovery in HEAs.