准二维双分散胶体玻璃结构与动力学研究

玻璃态广泛存在于自然与人工合成材料中。然而，其物理机制目前仍然是凝聚态物理有待解决的问题。其中，理解玻璃态中微观单元的结构与动力学之间的关系对理解玻璃态至关重要。本研究选取了玻璃态物理前沿领域中两个重要的动力学现象，通过胶体实验、数值模拟以及机器学习理解现象背后的结构物理机制。第一个现象为再进入玻璃化转变，指增加玻璃系统中粒子间吸引力引起的先融化再固化的非单调动力学转变。实验采用准二维双分散胶体玻璃，利用温敏表面活性剂胶束的排空效应，原位改变胶体间吸引力强度，在特定胶体体积分数区间发现了再进入玻璃化转变现象。为了厘清其结构机制，采用包括三角剖分在内的多种结构分析手段，发现吸引力增强使大粒子排列更紧密，进而增加了其他粒子的自由体积与迁移率，再次启动本已停滞的结构弛豫过程。同时，吸引力增强引发大粒子间键长变短，导致结构重排列更慢。上述两种效果相反的机制产生竞争，造成了非单调的动力学现象，构成了再进入玻璃化转变定性半定量的结构机制。第二个研究针对玻璃弛豫过程中的准空位现象。准空位是玻璃中协助粒子线状运动的局域自由体积，可通过线状运动的起始与终止状态判断。本研究尝试为准空位提供基于静态局域结构的诠释。研究利用数值模拟玻璃的粒子轨迹信息，训练二元分类支持向量机模型预测准空位位置。完成训练的模型在测试中实现了高于85%的测试分数，证明准空位与粒子局域结构具有强相关性，为建立准空位结构机制提供了重要支持。

Glass is ubiquitous in natural and artificial materials. However, the mechanism of glass transition remains unsolved in condensed matter physics; it requires understanding the connection between structure of its microscopic constituent components and their dynamics. In this thesis, we investigate two important phenomena in glassy state. We employ colloidal experiments, computer simulation, and machine learning methods to study the structural mechanism. The first problem is called reentrant glass transition: a glass state first melts and then freezes again upon increasing the inter-particle attraction strength. In our experiment, we use quasi-two-dimensional bidisperse colloidal samples with tunable depletion attraction strength, and locate reentrant glass transition in its phase diagram. To understand the underlying structural mechanism, we carry out a systematic structural analysis. Our results show that when increasing the attraction strength, big particles exhibit denser packing so that other particles in the neighborhood gain more free volume; this enhances their mobility and ultimately melts the glass. Meanwhile, the bonds between big particles are observed to become stronger and eventually freeze the sample at strong attraction strength. These two mechanisms with opposite effects result in a non-monotonic dynamical transition. Thus, our result provides a semi-quantitative mechanism for the reentrant glass transition. The second study focuses on quasi-void in glass relaxation dynamics. Quai-voids are the free volume in glass which facilitate stringlike motions of particles; they can be determined by following the stringlike particle movements but a structural signature is still lacking. We try to provide a static structural explanation for quasi-voids in glasses. Support vector machine model is trained using data from simulations of binary glasses to predict quasi-voids’ location. The trained model achieves good testing scores (>85%), indicating structural features of quasi-voids are strongly correlated with their dynamics; this finding confirms our hypothesis that quasi-voids can be determined by static structure and this correlation can be utilized to predict quasi-voids.

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