Jamming and Disorder in Hard-Particle Packings

由首席研究员塞尔瓦托·托夸托和他在该部门的同事们提出的一项研究计划。普林斯顿化学与材料研究所致力于研究堵塞的硬颗粒堆积的统计几何学。提出了一种高效而新颖的算法来生成硬球、硬椭球和硬球柱面在二维和三维中的单分散和多分散填充。特别是，努力改进Lubachevsky-Stillinger和Zinchenko打包算法。将设计一个随机化的线性规划算法来测试所生成的硬粒子系统中的“干扰”类别，以及处理不可穿透性约束中的非线性的扩展。通过使用高质量的标量顺序指标和收集Voronoi镶嵌统计数据，将对生成的拥堵包装进行统计表征。最后，上述工具和算法将被用于研究受干扰结构的空间，包括最大随机干扰(MRJ)状态和低密度受干扰堆积的识别。该研究可能会产生重要的科学进展和结果。这个多学科项目加入了统计物理、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，但这项任务总体上为计算科学界提供了巨大的回报。由于颗粒填充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，所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.