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Composition and concentration anomalies for structure and dynamics of Gaussian-core mixtures

机译:高斯核混合物结构和动力学的组成和浓度异常

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We report molecular dynamics simulation results for two-component fluid mixtures of Gaussian-core particles, focusing on how tracer diffusivities and static pair correlations depend on temperature, particle concentration, and composition. At low particle concentrations, these systems behave like simple atomic mixtures. However, for intermediate concentrations, the single-particle dynamics of the two species largely decouple, giving rise to the following anomalous trends. Increasing either the concentration of the fluid (at fixed composition) or the mole fraction of the larger particles (at fixed particle concentration) enhances the tracer diffusivity of the larger particles but decreases that of the smaller particles. In fact, at sufficiently high particle concentrations, the larger particles exhibit higher mobility than the smaller particles. Each of these dynamic behaviors is accompanied by a corresponding structural trend that characterizes how either concentration or composition affects the strength of the static pair correlations. Specifically, the dynamic trends observed here are consistent with a single empirical scaling law that relates an appropriately normalized tracer diffusivity to its pair-correlation contribution to the excess entropy.
机译:我们报告了高斯核粒子的两组分流体混合物的分子动力学模拟结果,重点是示踪剂扩散率和静态对相关性如何取决于温度,粒子浓度和组成。在低粒子浓度下,这些系统的行为就像简单的原子混合物。但是,对于中等浓度,两个物种的单粒子动力学在很大程度上解耦,从而引起以下异常趋势。增加流体的浓度(在固定的组成下)或增大较大颗粒的摩尔分数(在固定的颗粒浓度下)会增加较大颗粒的示踪剂扩散率,但会减小较小颗粒的示踪剂扩散率。实际上,在足够高的颗粒浓度下,较大的颗粒显示出比较小的颗粒更高的迁移率。这些动态行为的每一个都伴随着一个相应的结构趋势,该趋势表征了浓度或组成如何影响静态对相关性的强度。具体而言,此处观察到的动态趋势与单个经验缩放定律相一致,该定律将适当归一化的示踪剂扩散率与其对过量熵的对相关贡献相关联。

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