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

Pei En Chen, Rahul Raghavan, Yu Zheng, Hechao Li, Kumar Ankit, Yang Jiao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We devise reduced-dimension metrics for effectively measuring the distance between two points (i.e., microstructures) in the microstructure space and quantifying the pathway associated with microstructural evolution, based on a recently introduced set of hierarchical n-point polytope functions Pn. The Pn functions provide the probability of finding particular n-point configurations associated with regular n polytopes in the material system, and are a special subset of the standard n-point correlation functions Sn that effectively decompose the structural features in the system into regular polyhedral basis with different symmetries. The nth order metric ωn is defined as the L1 norm associated with the Pn functions of two distinct microstructures. By choosing a reference initial state (i.e., a microstructure associated with t0=0), the ωn(t) metrics quantify the evolution of distinct polyhedral symmetries and can in principle capture emerging polyhedral symmetries that are not apparent in the initial state. To demonstrate their utility, we apply the ωn metrics to a two-dimensional binary system undergoing spinodal decomposition to extract the phase separation dynamics via the temporal scaling behavior of the corresponding ωn(t), which reveals mechanisms governing the evolution. Moreover, we employ ωn(t) to analyze pattern evolution during vapor deposition of phase-separating alloy films with different surface contact angles, which exhibit rich evolution dynamics including both unstable and oscillating patterns. The ωn metrics have potential applications in establishing quantitative processing-structure-property relationships, as well as real-time processing control and optimization of complex heterogeneous material systems.

Original languageEnglish (US)
Article number025306
JournalPhysical Review E
Volume105
Issue number2
DOIs
StatePublished - Feb 2022

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

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