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Related Concept Videos

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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Three-dimensional strain analysis is crucial for understanding how materials deform under stress, particularly in elastic, homogeneous materials. This method employs principal stress axes to simplify complex stress states into more understandable forms. Subjected to stress, a small cubic element within a material either expands or contracts along these axes, transforming into a rectangular parallelepiped. This transformation effectively illustrates the material's deformation. The principal...
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Electron Configuration of Multielectron Atoms03:26

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The alkali metal sodium (atomic number 11) has one more electron than the neon atom. This electron must go into the lowest-energy subshell available, the 3s orbital, giving a 1s22s22p63s1 configuration. The electrons occupying the outermost shell orbital(s) (highest value of n) are called valence electrons, and those occupying the inner shell orbitals are called core electrons. Since the core electron shells correspond to noble gas electron configurations, we can abbreviate electron...
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Transition metals are defined as those elements that have partially filled d orbitals. As shown in Figure 1, the d-block elements in groups 3–12 are transition elements. The f-block elements, also called inner transition metals (the lanthanides and actinides), also meet this criterion because the d orbital is partially occupied before the f orbitals.
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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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When analyzing two planes intersecting at right angles under the influence of shearing, tensile, and compressive stresses, it is essential to identify principal planes, maximum shearing stress, and principal stresses. To find the principal planes, apply a formula that equates them to twice the shearing stress divided by the difference between tensile and compressive stresses.
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Phase Selection Rules of Multi-Principal Element Alloys.

Lin Wang1, Bin Ouyang1

  • 1Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL, 32304, USA.

Advanced Materials (Deerfield Beach, Fla.)
|October 31, 2023
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Summary

This study introduces a new descriptor for predicting phase stability in multi-principal element alloys (MPEAs), improving accuracy and HCP phase prediction. This advances computational materials science for alloy discovery.

Keywords:
multiprincipal element alloysphase selectionsymbolic machine learning

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Area of Science:

  • Computational Materials Science
  • Alloy Design
  • Data-Driven Discovery

Background:

  • Computational prediction of phase stability in multi-principal element alloys (MPEAs) is crucial for materials discovery.
  • Existing methods using phenomenological theory and machine learning face limitations due to insufficient data and model interpretability.
  • Accurate prediction is essential for navigating the vast MPEA design space.

Purpose of the Study:

  • To develop a comprehensive dataset and a novel predictive descriptor for MPEA phase stability.
  • To improve the accuracy and interpretability of computational predictions in MPEA research.
  • To establish a foundation for data-driven exploration of MPEAs.

Main Methods:

  • Generated a dataset of 72,387 density functional theory calculations for MPEAs.
  • Developed a new global phenomenological descriptor based on atomic electronegativity and valence electron concentration.
  • Evaluated the descriptor's performance against existing methods, including valence electron concentration.

Main Results:

  • The new phase selection descriptor achieved an f1 score of 63%, outperforming the widely used valence electron concentration (47%).
  • The descriptor demonstrated superior recall for predicting the hexagonal close-packed (HCP) phase (0.48 vs. 0).
  • Data mining of 61,425 quaternary MPEAs provided physical interpretation and a computational science foundation.

Conclusions:

  • The developed descriptor offers a more accurate and interpretable approach for predicting MPEA phase stability.
  • This work significantly enhances the capability for rapid, data-driven exploration of MPEA design space.
  • The findings provide a solid foundation for autonomous discovery of advanced MPEAs with superior properties.