TY - JOUR

T1 - Quantifying microstructural evolution via time-dependent reduced-dimension metrics based on hierarchical n -point polytope functions

AU - Chen, Pei En

AU - Raghavan, Rahul

AU - Zheng, Yu

AU - Li, Hechao

AU - Ankit, Kumar

AU - Jiao, Yang

N1 - Funding Information:
This work is supported by National Science Foundation, Division of Material Research under Grant No. 2020277 (AI Institute: Planning: Novel Neural Architectures for 4D Materials Science). This research used computational resources of the Agave Research Computer Cluster of ASU. Y.J. acknowledges the generous support from ASU during his sabbatical leave.
Publisher Copyright:
© 2022 American Physical Society.

PY - 2022/2

Y1 - 2022/2

N2 - 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.

AB - 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.

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U2 - 10.1103/PhysRevE.105.025306

DO - 10.1103/PhysRevE.105.025306

M3 - Article

C2 - 35291075

AN - SCOPUS:85124648678

SN - 1539-3755

VL - 105

JO - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

JF - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

IS - 2

M1 - 025306

ER -