Jamming and Disorder in Hard-Particle Packings

普林斯顿材料研究所化学系的首席研究员Salvatore Torquato和他的同事们开展了一个研究项目，旨在研究被堵塞的硬颗粒填料的统计几何。开发了一种高效的新算法来生成二维和三维的硬球体、硬椭球和硬球柱的单分散和多分散填料。特别是，努力的方向是改进Lubachevsky-Stillinger和Zinchenko打包算法。将设计一种随机线性规划算法来测试生成的硬粒子系统中的“干扰”类别，以及扩展以处理不可穿透性约束中的非线性。生成的堵塞填料将通过使用高质量标量阶数和收集Voronoi镶嵌统计来统计特征。最后，将采用上述工具和算法对结构空间进行研究，包括最大随机卡滞（MRJ）状态和低密度卡滞填料的识别。重要的科学进步和成果可能会从拟议的研究中出现。这个多学科项目结合了统计物理、材料科学、几何、应用数学和高性能计算社区。统一这些不同的观点和目标是一项具有挑战性的任务，但总的来说，它为计算科学界提供了巨大的回报。由于颗粒填料被广泛用作颗粒材料、玻璃、液体和其他随机介质的简单模型，因此提出的研究将对新材料和纳米技术的发展具有重要意义。

ABSTRACTA research program by Salvatore Torquato, the principal investigator, and his colleagues in the Dept. of Chemistry & Princeton Materials Institute is aimed at studying the statistical geometry of jammed hard-particle packings. Efficient and novel algorithms are developed to generate monodisperse and polydisperse packings of hard spheres, hard ellipsoids, and hard spherocylinders in two and three dimensions. In particular, efforts are directed toward improving the Lubachevsky-Stillinger and Zinchenko packing algorithms. A randomized linear-programming algorithm will be devised to test for ``jamming'' categories in the generated hard-particle systems, as well as extensions to deal with nonlinearities in the impenetrability constraints. The generated jammed packings will be statistically characterized by using high-quality scalar order metrics and by collecting Voronoi tessellation statistics. Finally, the aforementioned tools and algorithms will be employed to investigate the space of jammed structures, including the identification of the maximally random jammed (MRJ) state and low-density jammed packings.Important scientific advances and outcomes are likely to emerge from the proposed research. This multidisciplinary project joins the statistical physics, materials science, geometry, applied mathematics, and high-performance computing communities. Unifying these different perspectives and goals is a challenging task, but one that offers great rewards to the computational-science community in general. Because particle packings are widely used as simple models for granular materials, glasses, liquids, and other random media, the proposed research will have important implications for the development of novel materials as well as nanotechnology.