Quantifying Microstructural Evolution via Time-Dependent Reduced-Dimension Metrics Based on Hierarchical n-Point Polytope Functions
Quantifying Microstructural Evolution via Time-Dependent Reduced-Dimension Metrics Based on Hierarchical n-Point Polytope Functions
复制标题
通过基于分层 n 点多面体函数的时变降维度量来量化微观结构演化
DOI:
10.1103/physreve.105.025306
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发表时间:
2022
期刊:
影响因子:
--
通讯作者:
Jiao, Y
中科院分区:
文献类型:
--
作者:
Chen, P.;Raghavan, R;Zheng, Y;Li, H.;Ankit, K.;Jiao, Y
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-point polytope functions. Thefunctions provide the probability of finding particular-point configurations associated with regularpolytopes in the material system, and are a special subset of the standard-point correlation functionsthat effectively decompose the structural features in the system into regular polyhedral basis with different symmetries. Theorder metricis defined as thenorm associated with thefunctions of two distinct microstructures. By choosing a reference initial state (i.e., a microstructure associated with), themetrics 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 themetrics to a two-dimensional binary system undergoing spinodal decomposition to extract the phase separation dynamics via the temporal scaling behavior of the corresponding, which reveals mechanisms governing the evolution. Moreover, we employto 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. Themetrics have potential applications in establishing quantitative processing-structure-property relationships, as well as real-time processing control and optimization of complex heterogeneous material systems.