Dynamic heterogeneity in polydisperse systems: A comparative study of the role of local structural order parameter and particle size

In polydisperse systems, describing the structure and any structural order parameter (SOP) is not trivial as it varies with the number of species we use to describe the system, M. Depending on the degree of polydispersity, there is an optimum value of M = M-0 where we show that the mutual information of the system increases. However, surprisingly, the correlation between a recently proposed SOP and the dynamics is highest for M = 1. This effect increases with polydispersity. We find that the SOP at M = 1 is coupled with the particle size, s, and this coupling increases with polydispersity and decreases with an increase in M. Careful analysis shows that at lower polydispersities, the SOP is a good predictor of the dynamics. However, at higher polydispersity, the dynamics is strongly dependent on s. Since the coupling between the SOP and s is higher for M = 1, it appears to be a better predictor of the dynamics. We also study the Vibrality, an order parameter independent of structural information. Compared to SOP, at high polydispersity, we find Vibrality to be a marginally better predictor of the dynamics. However, this high predictive power of Vibrality, which is not there at lower polydispersity, appears to be due to its stronger coupling with s. Therefore, our study suggests that for systems with high polydispersity, the correlation of any order parameter and s will affect the correlation between the order parameter and dynamics and need not project a generic predictive power of the order parameter.

Automated summary

In polydisperse systems, the correlation between the structural order parameter (SOP) and dynamics depends on the number of species used to describe the system. The SOP at M = 1 shows the highest correlation with dynamics in highly polydisperse systems. The study also suggests that the Vibrality, an order parameter independent of structural information, can be a slightly better predictor of dynamics at high polydispersity due to its strong coupling with particle size.