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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....
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Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

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Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
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Complexation Equilibria: Factors Influencing Stability of Complexes01:09

Complexation Equilibria: Factors Influencing Stability of Complexes

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In complexation reactions, metal cations are the electron pair acceptors, and the ligands are the electron pair donors. The stability of the metal complexes depends primarily on the complexing ability of the central metal ion and the nature of the ligands. Generally, the complexing ability of the metal ion depends on the size and charge of the ion. As the metal ion size increases, the stability of the metal complexes decreases, provided that the valency of the metal ion and the ligands remain...
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Bonding in Metals02:32

Bonding in Metals

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Metallic bonds are formed between two metal atoms. A simplified model to describe metallic bonding has been developed by Paul Drüde called the “Electron Sea Model”. 
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Valence Bond Theory02:42

Valence Bond Theory

9.8K
Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...
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Molecular Models02:00

Molecular Models

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Related Experiment Video

Updated: Sep 29, 2025

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

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Modeling environment-dependent atomic-level properties in complex-concentrated alloys.

Mackinzie S Farnell1, Zachary D McClure2, Shivam Tripathi2

  • 1School of Materials Science and Engineering, University of California Berkeley, Berkeley, California 94720, USA.

The Journal of Chemical Physics
|March 23, 2022
PubMed
Summary
This summary is machine-generated.

This study develops machine learning models for complex concentrated alloys (CCAs). These models accurately predict atomic properties, aiding in the design of advanced materials for high-temperature and radiation environments.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Alloy Design

Background:

  • Complex concentrated alloys (CCAs) exhibit desirable properties like high-temperature strength and radiation tolerance.
  • Their multi-principal component nature leads to complex atomic structures and properties, posing modeling challenges.

Purpose of the Study:

  • To develop accurate and computationally inexpensive predictive models for atomic properties of CrFeCoNiCu-based CCAs.
  • To enable the integration of these models into macroscopic simulations for broader material design applications.

Main Methods:

  • Combined atomistic simulations with many-body potentials and machine learning.
  • Developed atomic environment fingerprints using local geometry invariants and elemental information.
  • Utilized unrelaxed atomic structures for computationally efficient descriptors.

Main Results:

  • Achieved accurate predictive models for vacancy formation energy, cohesive energy, pressure, and volume in CCAs.
  • Demonstrated the models' ability to extrapolate to unseen compositions and elements.
  • Validated the computational efficiency of the descriptors for integration into larger-scale simulations.

Conclusions:

  • Machine learning combined with atomistic simulations provides a powerful approach for modeling complex concentrated alloys.
  • The developed models offer a computationally efficient pathway for predicting CCA properties and accelerating materials discovery.
  • The findings facilitate the design of novel CCAs with tailored properties for demanding applications.