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，应用于无序材料的建模。将被研究的范例系统是“自旋玻璃”。自旋玻璃是一种磁性材料，由于微观层面上的相互竞争作用，它以复杂的方式有序。自旋玻璃的理论模型包含了这些竞争相互作用，并重现了这些材料中看到的许多复杂现象。自旋玻璃模型还在神经科学和进化生物学中得到了应用，并被证明与计算机科学和工业工程中出现的困难优化问题有着密切的联系。PI的团队将开发并使用一种名为种群退火法的强大的新计算机算法来研究自旋眼镜和相关模型。种群退火法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优化问题。该项目将涉及计算研究的研究生和本科生。学生将学习统计物理中的先进计算方法和现代技术。技术总结该奖项支持计算材料科学在无序系统中的应用方面的研究和教育。人口退火法蒙特卡罗算法是将被开发和使用的主要计算工具。主要将重点放在自旋玻璃模型上，但其他要研究的系统包括硬方/硬球流体。玻璃系统带来了巨大的智力和计算挑战，几十年来一直备受争议。了解自旋玻璃的低温性质是材料科学的一个基本问题。基于布居退火法的大规模计算方法与新的分析方法相结合，有望阐明伊辛自旋玻璃低温相的性质，并区分相互竞争的理论。自旋玻璃模型与组合优化问题密切相关，因此对自旋玻璃的理解可以揭示许多其他的计算难题。例如，自旋玻璃中的温度混沌与使用并行回火、布居退火法或量子退火法等启发式方法寻找基态的计算难度密切相关。布居退火法是统计物理中平衡模拟的一种新范式。种群退火法有望在化学、生物和计算机科学等领域得到广泛应用。它既可用于模拟热平衡，又可作为求解组合优化问题的启发式算法。这是一种非常适合分布式计算的大规模并行算法，因此可能会为在比以前更大的规模上解决计算物理中的问题铺平道路。将布居退火法与动力学蒙特卡罗等其他算法思想相结合，将扩大计算统计物理学可用于研究具有粗糙自由能景观的系统的有效工具的范围。该项目将让研究生和本科生参与计算研究。学生将学习统计物理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.