Computational Studies of Disordered Systems in Statistical Physics

非技术总结该奖项支持计算材料科学的研究和教育，并应用于无序材料的建模。 将要研究的范例系统是“自旋玻璃”。 自旋玻璃是由于微观水平上的竞争相互作用而以复杂方式有序的磁性材料。 自旋玻璃的理论模型结合了这些相互竞争的相互作用，并再现了这些材料中看到的许多复杂现象。 自旋玻璃模型在神经科学和进化生物学中也有应用，并与计算机科学和工业工程中出现的困难优化问题有密切联系。 PI的团队将开发和使用一种强大的新计算机算法，称为群体退火，以研究自旋玻璃和相关模型。群体退火在计算科学的许多领域都有潜在的应用，从化学和生物学到计算机科学中的最优化问题。该项目将涉及研究生和本科生的计算研究。 学生将学习统计物理学中的先进计算方法和现代技术。 该奖项支持计算材料科学的研究和教育，并将其应用于无序系统。 人口退火蒙特卡罗算法是主要的计算工具，将开发和使用。主要重点将是自旋玻璃模型，但其他系统进行研究，包括硬方/硬球流体。玻璃体系提出了巨大的智力和计算挑战，几十年来一直存在争议。自旋玻璃的低温性质是材料科学的基础问题。基于群体退火算法结合新的分析方法的大规模计算方法有望澄清伊辛自旋玻璃的低温相的性质，并区分竞争的理论。自旋玻璃模型与组合优化问题密切相关，理解自旋玻璃模型有助于解决许多其他的计算难题。例如，自旋玻璃中的温度混沌与使用平行回火、布居退火或量子退火等物理学方法寻找基态的计算难度密切相关。布居退火是统计物理学中平衡模拟的新范式。 群体退火算法有望在化学、生物学和计算机科学等广泛领域得到应用。它既可用于模拟热平衡，又可作为启发式算法用于求解组合优化问题。它是一种非常适合分布式计算的大规模并行算法，因此可能为在比以前更大的规模上解决计算物理学问题铺平道路。 结合人口退火与其他算法的想法，如动力学蒙特卡罗将扩大有效的工具范围，可用于计算统计物理学研究系统与粗糙的自由能景观。 该项目将涉及研究生和本科生在计算研究。 学生将学习统计物理学中的先进计算方法和现代技术。

NONTECHNICAL SUMMARYThis award supports research and education in computational materials science with application to modeling of disordered materials. The paradigmatic system that will be investigated is the "spin glass". Spin glasses are magnetic materials that order in a complex fashion due to competing interactions at the microscopic level. Theoretical models of spin glasses incorporate these competing interactions and reproduce many of the complex phenomena seen in these materials. Spin glass models have also found applications in neuroscience and evolutionary biology and turn out to have close connections to difficult optimization problems arising in computer science and industrial engineering. The PI's group will develop and use a powerful new computer algorithm called population annealing to study spin glasses and related models. Population annealing has potential applications in many areas of computational science ranging from chemistry and biology to optimization problems in computer science.The project will involve both graduate students and undergraduate students in computational studies. Students will learn advanced computational methods and modern techniques in statistical physics. TECHNICAL SUMMARYThis award supports research and education in computational materials science with applications to disordered systems. The population annealing Monte Carlo algorithm is the primary computational tool that will be developed and used. The main emphasis will be on spin glass models but other systems to be studied include the hard square/hard sphere fluids. Glassy systems present formidable intellectual and computational challenges and have remained controversial for decades. Understanding the low temperature properties of spin glasses is a foundational problem of materials science. The large scale computational approach based on the population annealing algorithm combined with new analysis methods promises to clarify the nature of the low temperature phase of Ising spin glasses and to distinguish between competing theories. Spin glass models are closely related to combinatorial optimization problems and understanding spin glasses thus could shed light on many other hard computational problems. For example, temperature chaos in spin glasses is closely related to the computational difficulty of finding ground states using heuristics such as parallel tempering, population annealing or quantum annealing.Population annealing is a new paradigm for equilibrium simulations in statistical physics. The population annealing algorithm promises to find application in a wide range of fields including chemistry, biology and computer science. It is useful both for simulating thermal equilibrium and as a heuristic for finding solutions for combinatorial optimization problems. It is a massively parallel algorithm well suited to distributed computing and thus may pave the way to solving problems in computational physics on a larger scale than previously possible. Combining population annealing with other algorithmic ideas such as kinetic Monte Carlo will expand the range of effective tools available to computational statistical physics for studying systems with rough free energy landscapes. The project will involve both graduate students and undergraduate students in computational studies. Students will learn advanced computational methods and modern techniques in statistical physics.