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Related Concept Videos

Metallic Solids02:37

Metallic Solids

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. Many...
Lattice Centering and Coordination Number02:33

Lattice Centering and Coordination Number

The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
Types of Unit Cells
Imagine taking a large number of identical...
Structures of Solids02:22

Structures of Solids

Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
Ionic Crystal Structures02:42

Ionic Crystal Structures

Ionic crystals consist of two or more different kinds of ions that usually have different sizes. The packing of these ions into a crystal structure is more complex than the packing of metal atoms that are the same size.
Most monatomic ions behave as charged spheres, and their attraction for ions of opposite charge is the same in every direction. Consequently, stable structures for ionic compounds result (1) when ions of one charge are surrounded by as many ions as possible of the opposite...
Coordination Number and Geometry02:57

Coordination Number and Geometry

For transition metal complexes, the coordination number determines the geometry around the central metal ion. Table 1 compares coordination numbers to molecular geometry. The most common structures of the complexes in coordination compounds are octahedral, tetrahedral, and square planar.
Network Covalent Solids02:18

Network Covalent Solids

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...

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Related Experiment Video

Updated: May 19, 2026

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses
08:55

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

Published on: June 7, 2018

A numerical coarse-grained description of a binary alloy.

J M Rickman1, T J Delph, E B Webb

  • 1Department of Materials Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, USA.

The Journal of Chemical Physics
|August 17, 2012
PubMed
Summary

This study uses Monte Carlo simulations to calculate the free energy of copper-nickel alloys. Histogram reweighting techniques allow for broad extrapolation of alloy properties across various conditions.

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Processing of Bulk Nanocrystalline Metals at the US Army Research Laboratory
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Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

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Related Experiment Videos

Last Updated: May 19, 2026

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses
08:55

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

Published on: June 7, 2018

Processing of Bulk Nanocrystalline Metals at the US Army Research Laboratory
08:58

Processing of Bulk Nanocrystalline Metals at the US Army Research Laboratory

Published on: March 7, 2018

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting
08:32

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

Published on: May 14, 2016

Area of Science:

  • Computational Materials Science
  • Thermodynamics
  • Alloy Physics

Background:

  • Understanding alloy behavior requires accurate free energy calculations.
  • Coarse-grained models simplify complex material interactions.
  • Embedded-atom methods provide a framework for atomic interactions.

Purpose of the Study:

  • To determine the Ginzburg-Landau free energy for a copper-nickel (Cu-Ni) alloy using computational methods.
  • To extrapolate free energy and thermodynamic properties over a wide parameter space.
  • To compare simulation results with established alloy models.

Main Methods:

  • Monte Carlo simulation in the semi-grand canonical ensemble.
  • Tabulated histogram of joint probability density for composition, energy, and volume.
  • Histogram reweighting techniques for extrapolation.
  • Analysis of thermodynamic quantities using joint cumulants.

Main Results:

  • The Ginzburg-Landau free energy for the Cu-Ni alloy was successfully determined.
  • Free energy was extrapolated across a range of parameters using limited simulations.
  • Expressions for thermodynamic quantities were derived and extrapolated.

Conclusions:

  • The computational approach provides a robust method for determining alloy free energy.
  • Histogram reweighting enables efficient exploration of alloy phase space.
  • The method allows for prediction of composition dependence on temperature and chemical potential.