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Metallic Solids02:37

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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.
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Simulating short-range order in compositionally complex materials.

Alberto Ferrari1, Fritz Körmann2,3, Mark Asta4,5

  • 1Materials Science and Engineering, Delft University of Technology, Delft, The Netherlands.

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|January 4, 2024
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Summary
This summary is machine-generated.

Short-range order (SRO) in complex materials is hard to measure but crucial for properties. New computational methods, including machine learning, are improving our understanding of SRO formation and its effects.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Condensed Matter Physics

Background:

  • Short-range order (SRO) involves atomic arrangements at the nanometer scale in multicomponent materials.
  • The impact of SRO on the properties of complex materials like alloys and ceramics is a significant area of research.
  • Experimental characterization of SRO is challenging, especially its nature and spatial extent.

Purpose of the Study:

  • To highlight advancements in computational approaches for quantifying and understanding SRO.
  • To discuss the challenges in simulating SRO in compositionally complex materials.
  • To recap key theoretical concepts and methods related to SRO.

Main Methods:

  • Utilizing atomistic simulations to access SRO information.
  • Employing machine learning-based interatomic potentials for accurate simulations.
  • Addressing challenges in sampling high-dimensional configuration spaces.

Main Results:

  • Progress in computational methods enables better quantification of SRO.
  • Machine learning potentials improve the accuracy of atomistic simulations for SRO.
  • New approaches facilitate the identification of conditions for SRO formation.

Conclusions:

  • Computational methods, particularly machine learning, are advancing the understanding of SRO in complex materials.
  • Improved simulation techniques are key to controlling material properties through SRO.
  • Further research is needed to fully elucidate the thermodynamic and kinetic aspects of SRO.