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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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Theory of Metallic Conduction01:17

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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.
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Plasticity00:58

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Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...
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Plastic Deformations01:19

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Plastic deformation represents a fundamental concept in materials science, which explains the irreversible change in the shape of a material when it experiences stress beyond its elastic capability. This phenomenon is important in structural engineering, especially in designing and analyzing cantilever beams—structures that are securely fixed at one end and bear loads at the opposite end. When these beams are subjected to loads within their elastic range, they will return to their...
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Three-Dimensional Analysis of Strain01:29

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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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Yield Criteria for Ductile Materials under Plane Stress01:25

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In designing structural elements and machine parts using ductile materials, it is crucial to ensure that these components withstand applied stresses without yielding. Yielding is initially determined through a tensile test, which evaluates the material's response to uniaxial stress. However, tensile stress is insufficient when components face biaxial or plane stress conditions This condition requires advanced criteria to predict failure.
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A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic

Qi Wang1, Anubhav Jain2

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Machine learning models predict atomic plastic sites in metallic glasses using structural data. This "quench-in softness" metric identifies sites prone to deformation, offering a general framework for understanding material properties.

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Computational Materials Science

Background:

  • Metallic glasses (MGs) exhibit non-uniform atomic plastic response under mechanical load.
  • The link between atomic structure and plastic heterogeneity in MGs is not fully understood.
  • Predicting localized plastic deformation in MGs remains a challenge.

Purpose of the Study:

  • To develop a machine learning (ML) model that predicts plastic sites in metallic glasses based on structural information.
  • To introduce a novel "quench-in softness" metric derived from interstice distributions.
  • To establish a generalizable framework for understanding site-specific properties in MGs.

Main Methods:

  • Utilizing novel site environment features characterizing interstice distributions around atoms.
  • Applying machine learning algorithms to identify plastic sites using only quenched structural data.
  • Training and testing the ML model on various Cu-Zr compositions and other MG systems (Ni-Nb, Al-Sm, Fe-P).

Main Results:

  • The ML model accurately identifies potential plastic sites based on structural information alone.
  • The "quench-in softness" metric predicts sites that activate at high strains, losing accuracy only with shear band formation.
  • A model trained on one composition generalizes well to different compositions and entirely different MG systems.

Conclusions:

  • A data-centric framework using ML and site environment features can reliably predict plastic sites in metallic glasses.
  • The "quench-in softness" metric provides a powerful tool for understanding the structural origins of plastic heterogeneity.
  • This approach offers a generalizable method for investigating site-specific properties in metallic glasses and potentially other amorphous materials.