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Quantifying microstructural evolution via time-dependent reduced-dimension metrics based on hierarchical n-point

Pei-En Chen1, Rahul Raghavan2, Yu Zheng3

  • 1Mechanical and Aerospace Engineering, Arizona State University, Tempe, Arizona 85287, USA.

Physical Review. E
|March 16, 2022
PubMed
Summary
This summary is machine-generated.

New metrics quantify microstructure evolution using hierarchical n-point polytope functions. These metrics reveal hidden symmetries and dynamics in materials, aiding in processing-structure-property relationship development.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Statistical Mechanics

Background:

  • Characterizing microstructural evolution is crucial for understanding material properties.
  • Traditional methods often struggle to capture complex, evolving symmetries.
  • Hierarchical n-point polytope functions (P_n) offer a novel approach to structural decomposition.

Purpose of the Study:

  • To develop reduced-dimension metrics (Ω_n) for quantifying microstructure distance and evolution pathways.
  • To utilize P_n functions to capture evolving polyhedral symmetries during material transformations.
  • To demonstrate the utility of Ω_n metrics in analyzing phase separation and thin-film deposition dynamics.

Main Methods:

  • Defined nth order metric Ω_n as the L1 norm of P_n functions between two microstructures.
  • Applied Ω_n metrics to a 2D binary system undergoing spinodal decomposition.
  • Analyzed pattern evolution in phase-separating alloy films using Ω_n(t) metrics.

Main Results:

  • Ω_n metrics effectively quantify microstructural evolution and reveal temporal scaling behavior.
  • Demonstrated the ability of Ω_n metrics to capture emerging polyhedral symmetries.
  • Successfully analyzed complex dynamics in spinodal decomposition and vapor deposition systems.

Conclusions:

  • The developed Ω_n metrics provide a powerful tool for analyzing complex microstructural evolution.
  • These metrics can establish quantitative processing-structure-property relationships.
  • Potential applications include real-time processing control and optimization of heterogeneous materials.