Quantifying microstructural evolution via timedependent reduceddimension metrics based on hierarchical n point polytope functions
Abstract
We devise reduceddimension 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 P_{n}. The P_{n} 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 S_{n} that effectively decompose the structural features in the system into regular polyhedral basis with different symmetries. The n th order metric Ω_{n} is defined as the L_{1} norm associated with the P_{n} functions of two distinct microstructures. By choosing a reference initial state (i.e., a microstructure associated with t_{0}=0 ), the Ω_{n}(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 Ω_{n} metrics to a twodimensional binary system undergoing spinodal decomposition to extract the phase separation dynamics via the temporal scaling behavior of the corresponding Ω_{n}(t ) , which reveals mechanisms governing the evolution. Moreover, we employ Ω_{n}(t ) to analyze pattern evolution during vapor deposition of phaseseparating alloy films with different surface contact angles, which exhibit rich evolution dynamics including both unstable and oscillating patterns. The Ω_{n} metrics have potential applications in establishing quantitative processingstructureproperty relationships, as well as realtime processing control and optimization of complex heterogeneous material systems.
 Publication:

Physical Review E
 Pub Date:
 February 2022
 DOI:
 10.1103/PhysRevE.105.025306
 arXiv:
 arXiv:2111.05926
 Bibcode:
 2022PhRvE.105b5306C
 Keywords:

 Physics  Computational Physics;
 Condensed Matter  Materials Science
 EPrint:
 11 pages 9 figures