New Numerical and Theoretical Methods to Analyse Disordered Materials

技术概述：该奖项支持自旋玻璃低温特性的计算和理论研究，以及受强无序材料启发的模型。这项研究部分基于信息技术研究计划支持的一个项目取得的进展。本项目涉及开发、应用和交流新的方法来探测无序系统和网络的结构和动态特性，利用重整化组和极限优化启发式的研究成果。研究的重点包括：1)将模拟需要高达10亿个变量的键以上和键附近渗流的受挫晶格，以预测低温指数，其准确性对揭示尺度关系和严格约束理论模型至关重要。2)发展新的元启发式；这对于玻璃材料的低能量特性、组合优化或复杂网络是必不可少的。3)利用数值和分析技术研究在低维和平均场行为之间转换的统计模型，以阐明玻璃中的尺度和临界维度，以及网络中的小世界特性。PI将为他的启发式和新的网络模型探索广泛的应用。这个奖项支持学生的教育，他们的学术训练是未来资讯科技基础建设的骨干。PI计划利用学生管理的集群计算机，在基于网络的环境中开发复杂的软件。所有结果和软件产品将通过出版物、演示和互联网广泛传播。这些项目将为学生提供统计物理方面的教育和研究经验。与洛斯阿拉莫斯国家实验室的研究人员的联系将为学生提供进一步的现实研究经验。非技术概述：该奖项支持统计物理学和计算机科学的计算和理论研究。这项研究的灵感来自于无序材料，在无序材料中，原子或最小的磁性单位不能被有效地视为排列在规则的晶体阵列中。这些模型被认为包含了这些材料的基本物理特性，是材料研究和计算机科学都知道的复杂系统的例子。这类问题包括蛋白质如何为生物功能找到最佳的原子配置、电路设计，以及一个看似简单的问题，即找到推销员只访问一次城市列表中的每个城市所必须走的最小距离。这些问题很难解决，因为它们包含相互冲突的约束，并且具有许多几乎正确的解决方案。PI开发了一种计算机算法，可以阐明无序磁性材料在低温下的性质。这个奖项支持这项工作的继续和PI的更广泛的应用。将计算机算法扩展到更广泛的一类问题，称为优化问题。这项研究部分基于信息技术研究计划支持的一个项目取得的进展。这个奖项支持学生的教育，他们的学术训练是未来资讯科技基础建设的骨干。PI计划利用学生管理的集群计算机，在基于网络的环境中开发复杂的软件。所有结果和软件产品将通过出版物、演示和互联网广泛传播。这些项目将为学生提供统计物理方面的教育和研究经验。与洛斯阿拉莫斯国家实验室的研究人员的联系将为学生提供进一步的现实研究经验。

TECHNICAL SUMMARY:This award supports computational and theoretical research on the low-temperature properties of spin glasses and models inspired by strongly disordered materials. The research is based in part on advances made on a project supported through the Information Technology Research initiative. This project involves developing, applying, and communicating new methods to probe structural and dynamical properties of disordered systems and networks, utilizing the renormalization group and the results of research on the Extremal Optimization heuristic. Thrusts of the research include: 1) Frustrated lattices requiring up to a billion variables above and near bond percolation will be simulated to predict low-temperature exponents with the accuracy essential to reveal scaling relations and to tightly constrain theoretical models. 2) New meta-heuristics will be developed; these are indispensable for low-energy properties of glassy materials, combinatorial optimization, or complex networks. 3) Investigating statistical models at the transition between low-dimensional and mean-field behavior using numerical and analytical techniques to elucidate scaling and critical dimensions in glasses, and small-world properties in networks. The PI will explore a wide range of applications for his heuristics and new network models.This award supports the education of students, whose academic training provides the future backbone of our information technology infrastructure. The PI plans to develop sophisticated software in a web-based environment, utilizing a student-administered cluster-computer. All results and software products will be disseminated widely through publications, presentations, and the internet. The projects will provide students with education and research experience in the study of statistical physics. Ties with researchers at Los Alamos National Laboratory will provide students further real-life research experience.NONTECHNICAL SUMMARY:This award supports computational and theoretical research at the interface of statistical physics and computer science. The research is inspired by disordered materials in which atoms or the smallest units of magnetism cannot usefully be viewed as being arranged in a regular crystalline array. The models that are believed to contain essential physics of these materials are examples of complex systems known both to materials research and computer science. Included in this class of problems are how proteins find the optimum configuration of atoms for biological function, circuit design, and the seemingly simple problem of finding the minimum distance a salesman must travel to visit each city of a list of cities only once. These are difficult to solve as they contain conflicting constraints and are characterized by many almost correct solutions. The PI has developed a computer algorithm that may elucidate the properties of disordered magnetic materials at low-temperatures. This award supports a continuation of that work and the more general application of the PI?s computer algorithm to a wider class of problems known as optimization problems. The research is based in part on advances made on a project supported through the Information Technology Research initiative. This award supports the education of students, whose academic training provides the future backbone of our information technology infrastructure. The PI plans to develop sophisticated software in a web-based environment, utilizing a student-administered cluster-computer. All results and software products will be disseminated widely through publications, presentations, and the internet. The projects will provide students with education and research experience in the study of statistical physics. Ties with researchers at Los Alamos National Laboratory will provide students further real-life research experience.